id	sid	tid	token	lemma	pos
fcis-7987	1	1	frontiers	frontier	NOUN
fcis-7987	1	2	in	in	ADP
fcis-7987	1	3	computing	computing	NOUN
fcis-7987	1	4	and	and	CCONJ
fcis-7987	1	5	intelligent	intelligent	ADJ
fcis-7987	1	6	systems	system	NOUN
fcis-7987	1	7	issn	issn	VERB
fcis-7987	1	8	:	:	PUNCT
fcis-7987	1	9	2832	2832	NUM
fcis-7987	1	10	-	-	SYM
fcis-7987	1	11	6024	6024	NUM
fcis-7987	1	12	|	|	NOUN
fcis-7987	1	13	vol	vol	NOUN
fcis-7987	1	14	.	.	PROPN
fcis-7987	2	1	3	3	NUM
fcis-7987	2	2	,	,	PUNCT
fcis-7987	2	3	no	no	INTJ
fcis-7987	2	4	.	.	NOUN
fcis-7987	2	5	3	3	NUM
fcis-7987	2	6	,	,	PUNCT
fcis-7987	2	7	2023	2023	NUM
fcis-7987	2	8	22	22	NUM
fcis-7987	2	9	research	research	NOUN
fcis-7987	2	10	on	on	ADP
fcis-7987	2	11	network	network	NOUN
fcis-7987	2	12	intrusion	intrusion	NOUN
fcis-7987	2	13	detection	detection	NOUN
fcis-7987	2	14	based	base	VERB
fcis-7987	2	15	on	on	ADP
fcis-7987	2	16	transformer	transformer	ADJ
fcis-7987	2	17	gang	gang	NOUN
fcis-7987	2	18	gan	gan	PROPN
fcis-7987	2	19	,	,	PUNCT
fcis-7987	2	20	weiju	weiju	PROPN
fcis-7987	2	21	kong	kong	PROPN
fcis-7987	2	22	school	school	PROPN
fcis-7987	2	23	of	of	ADP
fcis-7987	2	24	cyberspace	cyberspace	NOUN
fcis-7987	2	25	security	security	NOUN
fcis-7987	2	26	,	,	PUNCT
fcis-7987	2	27	chengdu	chengdu	PROPN
fcis-7987	2	28	university	university	PROPN
fcis-7987	2	29	of	of	ADP
fcis-7987	2	30	information	information	NOUN
fcis-7987	2	31	technology	technology	PROPN
fcis-7987	2	32	,	,	PUNCT
fcis-7987	2	33	chengdu	chengdu	PROPN
fcis-7987	2	34	610225	610225	NUM
fcis-7987	2	35	,	,	PUNCT
fcis-7987	2	36	china	china	PROPN
fcis-7987	2	37	.	.	PUNCT
fcis-7987	3	1	abstract	abstract	PROPN
fcis-7987	3	2	:	:	PUNCT
fcis-7987	3	3	with	with	ADP
fcis-7987	3	4	the	the	DET
fcis-7987	3	5	advancement	advancement	NOUN
fcis-7987	3	6	of	of	ADP
fcis-7987	3	7	technology	technology	NOUN
fcis-7987	3	8	,	,	PUNCT
fcis-7987	3	9	the	the	DET
fcis-7987	3	10	development	development	NOUN
fcis-7987	3	11	of	of	ADP
fcis-7987	3	12	various	various	ADJ
fcis-7987	3	13	industries	industry	NOUN
fcis-7987	3	14	has	have	AUX
fcis-7987	3	15	become	become	VERB
fcis-7987	3	16	inseparable	inseparable	ADJ
fcis-7987	3	17	from	from	ADP
fcis-7987	3	18	informatization	informatization	NOUN
fcis-7987	3	19	.	.	PUNCT
fcis-7987	4	1	people	people	NOUN
fcis-7987	4	2	's	's	PART
fcis-7987	4	3	lives	life	NOUN
fcis-7987	4	4	have	have	AUX
fcis-7987	4	5	become	become	VERB
fcis-7987	4	6	closely	closely	ADV
fcis-7987	4	7	related	relate	VERB
fcis-7987	4	8	to	to	ADP
fcis-7987	4	9	the	the	DET
fcis-7987	4	10	network	network	NOUN
fcis-7987	4	11	.	.	PUNCT
fcis-7987	5	1	while	while	SCONJ
fcis-7987	5	2	using	use	VERB
fcis-7987	5	3	the	the	DET
fcis-7987	5	4	network	network	NOUN
fcis-7987	5	5	to	to	PART
fcis-7987	5	6	facilitate	facilitate	VERB
fcis-7987	5	7	our	our	PRON
fcis-7987	5	8	lives	life	NOUN
fcis-7987	5	9	,	,	PUNCT
fcis-7987	5	10	massive	massive	ADJ
fcis-7987	5	11	data	datum	NOUN
fcis-7987	5	12	is	be	AUX
fcis-7987	5	13	also	also	ADV
fcis-7987	5	14	generated	generate	VERB
fcis-7987	5	15	.	.	PUNCT
fcis-7987	6	1	traditional	traditional	ADJ
fcis-7987	6	2	firewall	firewall	NOUN
fcis-7987	6	3	technologies	technology	NOUN
fcis-7987	6	4	are	be	AUX
fcis-7987	6	5	no	no	ADV
fcis-7987	6	6	longer	long	ADV
fcis-7987	6	7	sufficient	sufficient	ADJ
fcis-7987	6	8	to	to	PART
fcis-7987	6	9	meet	meet	VERB
fcis-7987	6	10	current	current	ADJ
fcis-7987	6	11	needs	need	NOUN
fcis-7987	6	12	.	.	PUNCT
fcis-7987	7	1	deep	deep	ADJ
fcis-7987	7	2	learning	learning	NOUN
fcis-7987	7	3	algorithms	algorithm	NOUN
fcis-7987	7	4	can	can	AUX
fcis-7987	7	5	establish	establish	VERB
fcis-7987	7	6	complex	complex	ADJ
fcis-7987	7	7	mapping	mapping	NOUN
fcis-7987	7	8	relationships	relationship	NOUN
fcis-7987	7	9	between	between	ADP
fcis-7987	7	10	network	network	NOUN
fcis-7987	7	11	data	datum	NOUN
fcis-7987	7	12	,	,	PUNCT
fcis-7987	7	13	and	and	CCONJ
fcis-7987	7	14	can	can	AUX
fcis-7987	7	15	extract	extract	VERB
fcis-7987	7	16	hidden	hide	VERB
fcis-7987	7	17	correlation	correlation	NOUN
fcis-7987	7	18	features	feature	NOUN
fcis-7987	7	19	between	between	ADP
fcis-7987	7	20	data	datum	NOUN
fcis-7987	7	21	features	feature	NOUN
fcis-7987	7	22	to	to	PART
fcis-7987	7	23	achieve	achieve	VERB
fcis-7987	7	24	data	datum	NOUN
fcis-7987	7	25	recognition	recognition	NOUN
fcis-7987	7	26	and	and	CCONJ
fcis-7987	7	27	prediction	prediction	NOUN
fcis-7987	7	28	.	.	PUNCT
fcis-7987	8	1	therefore	therefore	ADV
fcis-7987	8	2	,	,	PUNCT
fcis-7987	8	3	this	this	DET
fcis-7987	8	4	paper	paper	NOUN
fcis-7987	8	5	introduces	introduce	NOUN
fcis-7987	8	6	transformer	transformer	NOUN
fcis-7987	8	7	and	and	CCONJ
fcis-7987	8	8	bidirectional	bidirectional	ADJ
fcis-7987	8	9	long	long	ADJ
fcis-7987	8	10	short	short	ADJ
fcis-7987	8	11	-	-	PUNCT
fcis-7987	8	12	term	term	NOUN
fcis-7987	8	13	memory	memory	NOUN
fcis-7987	8	14	(	(	PUNCT
fcis-7987	8	15	bilstm	bilstm	NOUN
fcis-7987	8	16	)	)	PUNCT
fcis-7987	8	17	into	into	ADP
fcis-7987	8	18	the	the	DET
fcis-7987	8	19	field	field	NOUN
fcis-7987	8	20	of	of	ADP
fcis-7987	8	21	intrusion	intrusion	NOUN
fcis-7987	8	22	detection	detection	NOUN
fcis-7987	8	23	,	,	PUNCT
fcis-7987	8	24	and	and	CCONJ
fcis-7987	8	25	proposes	propose	VERB
fcis-7987	8	26	an	an	DET
fcis-7987	8	27	intrusion	intrusion	NOUN
fcis-7987	8	28	detection	detection	NOUN
fcis-7987	8	29	method	method	NOUN
fcis-7987	8	30	based	base	VERB
fcis-7987	8	31	on	on	ADP
fcis-7987	8	32	the	the	DET
fcis-7987	8	33	combination	combination	NOUN
fcis-7987	8	34	of	of	ADP
fcis-7987	8	35	transformer	transformer	NOUN
fcis-7987	8	36	-	-	PUNCT
fcis-7987	8	37	encoder	encoder	NOUN
fcis-7987	8	38	and	and	CCONJ
fcis-7987	8	39	bilstm	bilstm	NOUN
fcis-7987	8	40	(	(	PUNCT
fcis-7987	8	41	tbl	tbl	NOUN
fcis-7987	8	42	)	)	PUNCT
fcis-7987	8	43	.	.	PUNCT
fcis-7987	9	1	deep	deep	ADJ
fcis-7987	9	2	neural	neural	ADJ
fcis-7987	9	3	networks	network	NOUN
fcis-7987	9	4	(	(	PUNCT
fcis-7987	9	5	dnn	dnn	PROPN
fcis-7987	9	6	)	)	PUNCT
fcis-7987	9	7	are	be	AUX
fcis-7987	9	8	used	use	VERB
fcis-7987	9	9	to	to	PART
fcis-7987	9	10	further	far	ADV
fcis-7987	9	11	extract	extract	VERB
fcis-7987	9	12	data	data	NOUN
fcis-7987	9	13	features	feature	NOUN
fcis-7987	9	14	,	,	PUNCT
fcis-7987	9	15	and	and	CCONJ
fcis-7987	9	16	the	the	DET
fcis-7987	9	17	softmax	softmax	NOUN
fcis-7987	9	18	function	function	NOUN
fcis-7987	9	19	is	be	AUX
fcis-7987	9	20	used	use	VERB
fcis-7987	9	21	to	to	PART
fcis-7987	9	22	output	output	VERB
fcis-7987	9	23	classification	classification	NOUN
fcis-7987	9	24	results	result	NOUN
fcis-7987	9	25	.	.	PUNCT
fcis-7987	10	1	in	in	ADP
fcis-7987	10	2	order	order	NOUN
fcis-7987	10	3	to	to	PART
fcis-7987	10	4	verify	verify	VERB
fcis-7987	10	5	the	the	DET
fcis-7987	10	6	effectiveness	effectiveness	NOUN
fcis-7987	10	7	of	of	ADP
fcis-7987	10	8	this	this	DET
fcis-7987	10	9	method	method	NOUN
fcis-7987	10	10	,	,	PUNCT
fcis-7987	10	11	this	this	DET
fcis-7987	10	12	paper	paper	NOUN
fcis-7987	10	13	trains	train	NOUN
fcis-7987	10	14	and	and	CCONJ
fcis-7987	10	15	tests	test	VERB
fcis-7987	10	16	the	the	DET
fcis-7987	10	17	tbl	tbl	NOUN
fcis-7987	10	18	method	method	NOUN
fcis-7987	10	19	on	on	ADP
fcis-7987	10	20	the	the	DET
fcis-7987	10	21	nsl	nsl	NOUN
fcis-7987	10	22	-	-	PUNCT
fcis-7987	10	23	kdd	kdd	NOUN
fcis-7987	10	24	dataset	dataset	NOUN
fcis-7987	10	25	,	,	PUNCT
fcis-7987	10	26	and	and	CCONJ
fcis-7987	10	27	verifies	verifie	NOUN
fcis-7987	10	28	its	its	PRON
fcis-7987	10	29	feasibility	feasibility	NOUN
fcis-7987	10	30	and	and	CCONJ
fcis-7987	10	31	superiority	superiority	NOUN
fcis-7987	10	32	.	.	PUNCT
fcis-7987	11	1	keywords	keyword	NOUN
fcis-7987	11	2	:	:	PUNCT
fcis-7987	11	3	intrusion	intrusion	NOUN
fcis-7987	11	4	detection	detection	NOUN
fcis-7987	11	5	;	;	PUNCT
fcis-7987	11	6	deep	deep	ADJ
fcis-7987	11	7	learning	learning	NOUN
fcis-7987	11	8	,	,	PUNCT
fcis-7987	11	9	transformer	transformer	NOUN
fcis-7987	11	10	;	;	PUNCT
fcis-7987	11	11	bidirectional	bidirectional	ADJ
fcis-7987	11	12	long	long	ADJ
fcis-7987	11	13	short	short	ADJ
fcis-7987	11	14	-	-	PUNCT
fcis-7987	11	15	term	term	NOUN
fcis-7987	11	16	memory	memory	NOUN
fcis-7987	11	17	.	.	PUNCT
fcis-7987	12	1	1	1	X
fcis-7987	12	2	.	.	X
fcis-7987	12	3	introduction	introduction	NOUN
fcis-7987	12	4	as	as	ADP
fcis-7987	12	5	a	a	DET
fcis-7987	12	6	network	network	NOUN
fcis-7987	12	7	defense	defense	NOUN
fcis-7987	12	8	system	system	NOUN
fcis-7987	12	9	that	that	PRON
fcis-7987	12	10	can	can	AUX
fcis-7987	12	11	identify	identify	VERB
fcis-7987	12	12	network	network	NOUN
fcis-7987	12	13	attack	attack	NOUN
fcis-7987	12	14	behaviors	behavior	NOUN
fcis-7987	12	15	and	and	CCONJ
fcis-7987	12	16	recognize	recognize	VERB
fcis-7987	12	17	and	and	CCONJ
fcis-7987	12	18	alert	alert	VERB
fcis-7987	12	19	against	against	ADP
fcis-7987	12	20	them	they	PRON
fcis-7987	12	21	,	,	PUNCT
fcis-7987	12	22	intrusion	intrusion	NOUN
fcis-7987	12	23	detection	detection	NOUN
fcis-7987	12	24	system	system	NOUN
fcis-7987	12	25	(	(	PUNCT
fcis-7987	12	26	ids	id	NOUN
fcis-7987	12	27	)	)	PUNCT
fcis-7987	12	28	has	have	AUX
fcis-7987	12	29	become	become	VERB
fcis-7987	12	30	increasingly	increasingly	ADV
fcis-7987	12	31	important	important	ADJ
fcis-7987	12	32	in	in	ADP
fcis-7987	12	33	recent	recent	ADJ
fcis-7987	12	34	years	year	NOUN
fcis-7987	12	35	.	.	PUNCT
fcis-7987	13	1	artificial	artificial	ADJ
fcis-7987	13	2	intelligence	intelligence	NOUN
fcis-7987	13	3	has	have	AUX
fcis-7987	13	4	made	make	VERB
fcis-7987	13	5	significant	significant	ADJ
fcis-7987	13	6	breakthroughs	breakthrough	NOUN
fcis-7987	13	7	in	in	ADP
fcis-7987	13	8	many	many	ADJ
fcis-7987	13	9	fields	field	NOUN
fcis-7987	13	10	,	,	PUNCT
fcis-7987	13	11	especially	especially	ADV
fcis-7987	13	12	in	in	ADP
fcis-7987	13	13	data	data	NOUN
fcis-7987	13	14	recognition	recognition	NOUN
fcis-7987	13	15	and	and	CCONJ
fcis-7987	13	16	classification	classification	NOUN
fcis-7987	13	17	,	,	PUNCT
fcis-7987	13	18	where	where	SCONJ
fcis-7987	13	19	neural	neural	ADJ
fcis-7987	13	20	networks	network	NOUN
fcis-7987	13	21	with	with	ADP
fcis-7987	13	22	their	their	PRON
fcis-7987	13	23	powerful	powerful	ADJ
fcis-7987	13	24	feature	feature	NOUN
fcis-7987	13	25	extraction	extraction	NOUN
fcis-7987	13	26	and	and	CCONJ
fcis-7987	13	27	learning	learn	VERB
fcis-7987	13	28	capabilities	capability	NOUN
fcis-7987	13	29	can	can	AUX
fcis-7987	13	30	efficiently	efficiently	ADV
fcis-7987	13	31	identify	identify	VERB
fcis-7987	13	32	and	and	CCONJ
fcis-7987	13	33	classify	classify	VERB
fcis-7987	13	34	massive	massive	ADJ
fcis-7987	13	35	amounts	amount	NOUN
fcis-7987	13	36	of	of	ADP
fcis-7987	13	37	data	datum	NOUN
fcis-7987	13	38	in	in	ADP
fcis-7987	13	39	networks	network	NOUN
fcis-7987	13	40	.	.	PUNCT
fcis-7987	14	1	researchers	researcher	NOUN
fcis-7987	14	2	at	at	ADP
fcis-7987	14	3	home	home	ADV
fcis-7987	14	4	and	and	CCONJ
fcis-7987	14	5	abroad	abroad	ADV
fcis-7987	14	6	are	be	AUX
fcis-7987	14	7	studying	study	VERB
fcis-7987	14	8	the	the	DET
fcis-7987	14	9	most	most	ADV
fcis-7987	14	10	effective	effective	ADJ
fcis-7987	14	11	learning	learning	NOUN
fcis-7987	14	12	models	model	NOUN
fcis-7987	14	13	in	in	ADP
fcis-7987	14	14	different	different	ADJ
fcis-7987	14	15	scenarios	scenario	NOUN
fcis-7987	14	16	,	,	PUNCT
fcis-7987	14	17	but	but	CCONJ
fcis-7987	14	18	few	few	ADJ
fcis-7987	14	19	have	have	AUX
fcis-7987	14	20	studied	study	VERB
fcis-7987	14	21	how	how	SCONJ
fcis-7987	14	22	to	to	PART
fcis-7987	14	23	apply	apply	VERB
fcis-7987	14	24	neural	neural	ADJ
fcis-7987	14	25	networks	network	NOUN
fcis-7987	14	26	to	to	ADP
fcis-7987	14	27	network	network	NOUN
fcis-7987	14	28	intrusion	intrusion	NOUN
fcis-7987	14	29	detection	detection	NOUN
fcis-7987	14	30	.	.	PUNCT
fcis-7987	15	1	therefore	therefore	ADV
fcis-7987	15	2	,	,	PUNCT
fcis-7987	15	3	defending	defend	VERB
fcis-7987	15	4	against	against	ADP
fcis-7987	15	5	network	network	NOUN
fcis-7987	15	6	attacks	attack	NOUN
fcis-7987	15	7	is	be	AUX
fcis-7987	15	8	of	of	ADP
fcis-7987	15	9	great	great	ADJ
fcis-7987	15	10	significance	significance	NOUN
fcis-7987	15	11	for	for	ADP
fcis-7987	15	12	national	national	ADJ
fcis-7987	15	13	security	security	NOUN
fcis-7987	15	14	,	,	PUNCT
fcis-7987	15	15	enterprise	enterprise	NOUN
fcis-7987	15	16	development	development	NOUN
fcis-7987	15	17	,	,	PUNCT
fcis-7987	15	18	and	and	CCONJ
fcis-7987	15	19	personal	personal	ADJ
fcis-7987	15	20	privacy	privacy	NOUN
fcis-7987	15	21	protection	protection	NOUN
fcis-7987	15	22	.	.	PUNCT
fcis-7987	16	1	today	today	NOUN
fcis-7987	16	2	,	,	PUNCT
fcis-7987	16	3	with	with	ADP
fcis-7987	16	4	the	the	DET
fcis-7987	16	5	rapid	rapid	ADJ
fcis-7987	16	6	development	development	NOUN
fcis-7987	16	7	of	of	ADP
fcis-7987	16	8	network	network	NOUN
fcis-7987	16	9	technology	technology	NOUN
fcis-7987	16	10	,	,	PUNCT
fcis-7987	16	11	traditional	traditional	ADJ
fcis-7987	16	12	intrusion	intrusion	NOUN
fcis-7987	16	13	detection	detection	NOUN
fcis-7987	16	14	techniques	technique	NOUN
fcis-7987	16	15	have	have	AUX
fcis-7987	16	16	gradually	gradually	ADV
fcis-7987	16	17	become	become	VERB
fcis-7987	16	18	ineffective	ineffective	ADJ
fcis-7987	16	19	,	,	PUNCT
fcis-7987	16	20	and	and	CCONJ
fcis-7987	16	21	developing	develop	VERB
fcis-7987	16	22	new	new	ADJ
fcis-7987	16	23	network	network	NOUN
fcis-7987	16	24	intrusion	intrusion	NOUN
fcis-7987	16	25	detection	detection	NOUN
fcis-7987	16	26	models	model	NOUN
fcis-7987	16	27	has	have	AUX
fcis-7987	16	28	become	become	VERB
fcis-7987	16	29	a	a	DET
fcis-7987	16	30	top	top	ADJ
fcis-7987	16	31	priority	priority	NOUN
fcis-7987	16	32	.	.	PUNCT
fcis-7987	17	1	building	build	VERB
fcis-7987	17	2	an	an	DET
fcis-7987	17	3	intrusion	intrusion	NOUN
fcis-7987	17	4	detection	detection	NOUN
fcis-7987	17	5	system	system	NOUN
fcis-7987	17	6	based	base	VERB
fcis-7987	17	7	on	on	ADP
fcis-7987	17	8	deep	deep	ADJ
fcis-7987	17	9	learning	learning	NOUN
fcis-7987	17	10	has	have	VERB
fcis-7987	17	11	both	both	CCONJ
fcis-7987	17	12	theoretical	theoretical	ADJ
fcis-7987	17	13	research	research	NOUN
fcis-7987	17	14	value	value	NOUN
fcis-7987	17	15	and	and	CCONJ
fcis-7987	17	16	practical	practical	ADJ
fcis-7987	17	17	application	application	NOUN
fcis-7987	17	18	value	value	NOUN
fcis-7987	17	19	.	.	PUNCT
fcis-7987	18	1	this	this	DET
fcis-7987	18	2	paper	paper	NOUN
fcis-7987	18	3	proposes	propose	VERB
fcis-7987	18	4	an	an	DET
fcis-7987	18	5	intrusion	intrusion	NOUN
fcis-7987	18	6	detection	detection	NOUN
fcis-7987	18	7	method	method	NOUN
fcis-7987	18	8	that	that	PRON
fcis-7987	18	9	combines	combine	VERB
fcis-7987	18	10	entity	entity	NOUN
fcis-7987	18	11	embedding	embed	VERB
fcis-7987	18	12	and	and	CCONJ
fcis-7987	18	13	transformer	transformer	NOUN
fcis-7987	18	14	,	,	PUNCT
fcis-7987	18	15	using	use	VERB
fcis-7987	18	16	attention	attention	NOUN
fcis-7987	18	17	mechanisms	mechanism	NOUN
fcis-7987	18	18	to	to	PART
fcis-7987	18	19	identify	identify	VERB
fcis-7987	18	20	and	and	CCONJ
fcis-7987	18	21	classify	classify	VERB
fcis-7987	18	22	attack	attack	NOUN
fcis-7987	18	23	behaviors	behavior	NOUN
fcis-7987	18	24	in	in	ADP
fcis-7987	18	25	network	network	NOUN
fcis-7987	18	26	traffic	traffic	NOUN
fcis-7987	18	27	to	to	PART
fcis-7987	18	28	achieve	achieve	VERB
fcis-7987	18	29	the	the	DET
fcis-7987	18	30	goal	goal	NOUN
fcis-7987	18	31	of	of	ADP
fcis-7987	18	32	protecting	protect	VERB
fcis-7987	18	33	important	important	ADJ
fcis-7987	18	34	information	information	NOUN
fcis-7987	18	35	.	.	PUNCT
fcis-7987	19	1	the	the	DET
fcis-7987	19	2	nsl	nsl	PROPN
fcis-7987	19	3	-	-	PUNCT
fcis-7987	19	4	kdd	kdd	PROPN
fcis-7987	19	5	dataset	dataset	NOUN
fcis-7987	19	6	is	be	AUX
fcis-7987	19	7	used	use	VERB
fcis-7987	19	8	to	to	PART
fcis-7987	19	9	validate	validate	VERB
fcis-7987	19	10	the	the	DET
fcis-7987	19	11	performance	performance	NOUN
fcis-7987	19	12	of	of	ADP
fcis-7987	19	13	the	the	DET
fcis-7987	19	14	model	model	NOUN
fcis-7987	19	15	,	,	PUNCT
fcis-7987	19	16	and	and	CCONJ
fcis-7987	19	17	the	the	DET
fcis-7987	19	18	experimental	experimental	ADJ
fcis-7987	19	19	results	result	NOUN
fcis-7987	19	20	show	show	VERB
fcis-7987	19	21	that	that	SCONJ
fcis-7987	19	22	the	the	DET
fcis-7987	19	23	model	model	NOUN
fcis-7987	19	24	has	have	VERB
fcis-7987	19	25	good	good	ADJ
fcis-7987	19	26	detection	detection	NOUN
fcis-7987	19	27	performance	performance	NOUN
fcis-7987	19	28	.	.	PUNCT
fcis-7987	20	1	yang	yang	PROPN
fcis-7987	20	2	l	l	PROPN
fcis-7987	20	3	's	's	PART
fcis-7987	20	4	team	team	NOUN
fcis-7987	20	5	[	[	X
fcis-7987	20	6	1,2	1,2	NUM
fcis-7987	20	7	]	]	PUNCT
fcis-7987	20	8	proposed	propose	VERB
fcis-7987	20	9	the	the	DET
fcis-7987	20	10	application	application	NOUN
fcis-7987	20	11	of	of	ADP
fcis-7987	20	12	convolutional	convolutional	ADJ
fcis-7987	20	13	neural	neural	ADJ
fcis-7987	20	14	networks	network	NOUN
fcis-7987	20	15	to	to	ADP
fcis-7987	20	16	network	network	NOUN
fcis-7987	20	17	intrusion	intrusion	NOUN
fcis-7987	20	18	detection	detection	NOUN
fcis-7987	20	19	algorithms	algorithm	NOUN
fcis-7987	20	20	,	,	PUNCT
fcis-7987	20	21	and	and	CCONJ
fcis-7987	20	22	the	the	DET
fcis-7987	20	23	experimental	experimental	ADJ
fcis-7987	20	24	results	result	NOUN
fcis-7987	20	25	showed	show	VERB
fcis-7987	20	26	that	that	SCONJ
fcis-7987	20	27	the	the	DET
fcis-7987	20	28	method	method	NOUN
fcis-7987	20	29	not	not	PART
fcis-7987	20	30	only	only	ADV
fcis-7987	20	31	has	have	VERB
fcis-7987	20	32	generalization	generalization	NOUN
fcis-7987	20	33	but	but	CCONJ
fcis-7987	20	34	also	also	ADV
fcis-7987	20	35	improves	improve	VERB
fcis-7987	20	36	the	the	DET
fcis-7987	20	37	convergence	convergence	NOUN
fcis-7987	20	38	speed	speed	NOUN
fcis-7987	20	39	of	of	ADP
fcis-7987	20	40	the	the	DET
fcis-7987	20	41	model	model	NOUN
fcis-7987	20	42	.	.	PUNCT
fcis-7987	21	1	xiao	xiao	PROPN
fcis-7987	21	2	y	y	PROPN
fcis-7987	21	3	's	's	PART
fcis-7987	21	4	team	team	NOUN
fcis-7987	21	5	[	[	X
fcis-7987	21	6	3,4	3,4	NUM
fcis-7987	21	7	]	]	PUNCT
fcis-7987	21	8	proposed	propose	VERB
fcis-7987	21	9	a	a	DET
fcis-7987	21	10	simplified	simplified	ADJ
fcis-7987	21	11	residual	residual	ADJ
fcis-7987	21	12	network	network	NOUN
fcis-7987	21	13	algorithm	algorithm	NOUN
fcis-7987	21	14	to	to	PART
fcis-7987	21	15	reduce	reduce	VERB
fcis-7987	21	16	the	the	DET
fcis-7987	21	17	complexity	complexity	NOUN
fcis-7987	21	18	of	of	ADP
fcis-7987	21	19	the	the	DET
fcis-7987	21	20	network	network	NOUN
fcis-7987	21	21	and	and	CCONJ
fcis-7987	21	22	prevent	prevent	VERB
fcis-7987	21	23	the	the	DET
fcis-7987	21	24	problem	problem	NOUN
fcis-7987	21	25	of	of	ADP
fcis-7987	21	26	model	model	NOUN
fcis-7987	21	27	overfitting	overfitting	NOUN
fcis-7987	21	28	.	.	PUNCT
fcis-7987	22	1	the	the	DET
fcis-7987	22	2	algorithm	algorithm	NOUN
fcis-7987	22	3	further	far	ADV
fcis-7987	22	4	simplifies	simplify	VERB
fcis-7987	22	5	the	the	DET
fcis-7987	22	6	original	original	ADJ
fcis-7987	22	7	residual	residual	ADJ
fcis-7987	22	8	algorithm	algorithm	NOUN
fcis-7987	22	9	by	by	ADP
fcis-7987	22	10	deleting	delete	VERB
fcis-7987	22	11	one	one	NUM
fcis-7987	22	12	weight	weight	NOUN
fcis-7987	22	13	layer	layer	NOUN
fcis-7987	22	14	and	and	CCONJ
fcis-7987	22	15	two	two	NUM
fcis-7987	22	16	batch	batch	NOUN
fcis-7987	22	17	normalization	normalization	NOUN
fcis-7987	22	18	layers	layer	NOUN
fcis-7987	22	19	.	.	PUNCT
fcis-7987	23	1	although	although	SCONJ
fcis-7987	23	2	some	some	DET
fcis-7987	23	3	progress	progress	NOUN
fcis-7987	23	4	has	have	AUX
fcis-7987	23	5	been	be	AUX
fcis-7987	23	6	made	make	VERB
fcis-7987	23	7	in	in	ADP
fcis-7987	23	8	preventing	prevent	VERB
fcis-7987	23	9	overfitting	overfitting	NOUN
fcis-7987	23	10	,	,	PUNCT
fcis-7987	23	11	the	the	DET
fcis-7987	23	12	detection	detection	NOUN
fcis-7987	23	13	results	result	VERB
fcis-7987	23	14	for	for	ADP
fcis-7987	23	15	new	new	ADJ
fcis-7987	23	16	types	type	NOUN
fcis-7987	23	17	of	of	ADP
fcis-7987	23	18	network	network	NOUN
fcis-7987	23	19	attacks	attack	NOUN
fcis-7987	23	20	are	be	AUX
fcis-7987	23	21	not	not	PART
fcis-7987	23	22	ideal	ideal	ADJ
fcis-7987	23	23	.	.	PUNCT
fcis-7987	24	1	yu	yu	PROPN
fcis-7987	24	2	's	's	PART
fcis-7987	24	3	team	team	NOUN
fcis-7987	24	4	[	[	X
fcis-7987	24	5	5,6	5,6	X
fcis-7987	24	6	]	]	PUNCT
fcis-7987	24	7	proposed	propose	VERB
fcis-7987	24	8	a	a	DET
fcis-7987	24	9	bilstm	bilstm	NOUN
fcis-7987	24	10	(	(	PUNCT
fcis-7987	24	11	bidirectional	bidirectional	ADJ
fcis-7987	24	12	long	long	ADJ
fcis-7987	24	13	short	short	ADJ
fcis-7987	24	14	-	-	PUNCT
fcis-7987	24	15	term	term	NOUN
fcis-7987	24	16	memory	memory	NOUN
fcis-7987	24	17	)	)	PUNCT
fcis-7987	24	18	intrusion	intrusion	NOUN
fcis-7987	24	19	detection	detection	NOUN
fcis-7987	24	20	model	model	NOUN
fcis-7987	24	21	,	,	PUNCT
fcis-7987	24	22	which	which	PRON
fcis-7987	24	23	can	can	AUX
fcis-7987	24	24	capture	capture	VERB
fcis-7987	24	25	key	key	ADJ
fcis-7987	24	26	information	information	NOUN
fcis-7987	24	27	in	in	ADP
fcis-7987	24	28	the	the	DET
fcis-7987	24	29	feature	feature	NOUN
fcis-7987	24	30	information	information	NOUN
fcis-7987	24	31	to	to	PART
fcis-7987	24	32	complete	complete	VERB
fcis-7987	24	33	the	the	DET
fcis-7987	24	34	detection	detection	NOUN
fcis-7987	24	35	of	of	ADP
fcis-7987	24	36	abnormal	abnormal	ADJ
fcis-7987	24	37	data	datum	NOUN
fcis-7987	24	38	.	.	PUNCT
fcis-7987	25	1	although	although	SCONJ
fcis-7987	25	2	it	it	PRON
fcis-7987	25	3	fully	fully	ADV
fcis-7987	25	4	learns	learn	VERB
fcis-7987	25	5	the	the	DET
fcis-7987	25	6	temporal	temporal	ADJ
fcis-7987	25	7	information	information	NOUN
fcis-7987	25	8	of	of	ADP
fcis-7987	25	9	attack	attack	NOUN
fcis-7987	25	10	behavior	behavior	NOUN
fcis-7987	25	11	,	,	PUNCT
fcis-7987	25	12	it	it	PRON
fcis-7987	25	13	did	do	AUX
fcis-7987	25	14	not	not	PART
fcis-7987	25	15	consider	consider	VERB
fcis-7987	25	16	the	the	DET
fcis-7987	25	17	impact	impact	NOUN
fcis-7987	25	18	of	of	ADP
fcis-7987	25	19	historical	historical	ADJ
fcis-7987	25	20	information	information	NOUN
fcis-7987	25	21	,	,	PUNCT
fcis-7987	25	22	but	but	CCONJ
fcis-7987	25	23	still	still	ADV
fcis-7987	25	24	provides	provide	VERB
fcis-7987	25	25	a	a	DET
fcis-7987	25	26	new	new	ADJ
fcis-7987	25	27	idea	idea	NOUN
fcis-7987	25	28	for	for	ADP
fcis-7987	25	29	intrusion	intrusion	NOUN
fcis-7987	25	30	detection	detection	NOUN
fcis-7987	25	31	.	.	PUNCT
fcis-7987	26	1	research	research	NOUN
fcis-7987	26	2	experiments	experiment	NOUN
fcis-7987	26	3	have	have	AUX
fcis-7987	26	4	shown	show	VERB
fcis-7987	26	5	that	that	SCONJ
fcis-7987	26	6	the	the	DET
fcis-7987	26	7	imbalance	imbalance	NOUN
fcis-7987	26	8	of	of	ADP
fcis-7987	26	9	a	a	DET
fcis-7987	26	10	dataset	dataset	NOUN
fcis-7987	26	11	has	have	VERB
fcis-7987	26	12	a	a	DET
fcis-7987	26	13	significant	significant	ADJ
fcis-7987	26	14	impact	impact	NOUN
fcis-7987	26	15	on	on	ADP
fcis-7987	26	16	the	the	DET
fcis-7987	26	17	final	final	ADJ
fcis-7987	26	18	training	training	NOUN
fcis-7987	26	19	results	result	NOUN
fcis-7987	26	20	of	of	ADP
fcis-7987	26	21	a	a	DET
fcis-7987	26	22	model	model	NOUN
fcis-7987	26	23	.	.	PUNCT
fcis-7987	27	1	imbalanced	imbalanced	ADJ
fcis-7987	27	2	data	datum	NOUN
fcis-7987	27	3	can	can	AUX
fcis-7987	27	4	result	result	VERB
fcis-7987	27	5	in	in	ADP
fcis-7987	27	6	a	a	DET
fcis-7987	27	7	higher	high	ADJ
fcis-7987	27	8	proportion	proportion	NOUN
fcis-7987	27	9	of	of	ADP
fcis-7987	27	10	minority	minority	NOUN
fcis-7987	27	11	class	class	NOUN
fcis-7987	27	12	samples	sample	NOUN
fcis-7987	27	13	in	in	ADP
fcis-7987	27	14	the	the	DET
fcis-7987	27	15	dataset	dataset	NOUN
fcis-7987	27	16	,	,	PUNCT
fcis-7987	27	17	which	which	PRON
fcis-7987	27	18	can	can	AUX
fcis-7987	27	19	significantly	significantly	ADV
fcis-7987	27	20	affect	affect	VERB
fcis-7987	27	21	the	the	DET
fcis-7987	27	22	accuracy	accuracy	NOUN
fcis-7987	27	23	of	of	ADP
fcis-7987	27	24	the	the	DET
fcis-7987	27	25	model	model	NOUN
fcis-7987	27	26	's	's	PART
fcis-7987	27	27	predictions	prediction	NOUN
fcis-7987	27	28	.	.	PUNCT
fcis-7987	28	1	some	some	DET
fcis-7987	28	2	scholars	scholar	NOUN
fcis-7987	28	3	have	have	AUX
fcis-7987	28	4	begun	begin	VERB
fcis-7987	28	5	researching	research	VERB
fcis-7987	28	6	the	the	DET
fcis-7987	28	7	issue	issue	NOUN
fcis-7987	28	8	of	of	ADP
fcis-7987	28	9	data	datum	NOUN
fcis-7987	28	10	balancing	balancing	NOUN
fcis-7987	28	11	.	.	PUNCT
fcis-7987	29	1	the	the	DET
fcis-7987	29	2	bedi	bedi	PROPN
fcis-7987	30	1	[	[	X
fcis-7987	30	2	7	7	NUM
fcis-7987	30	3	]	]	X
fcis-7987	30	4	team	team	NOUN
fcis-7987	30	5	used	use	VERB
fcis-7987	30	6	siamese	siamese	ADJ
fcis-7987	30	7	neural	neural	ADJ
fcis-7987	30	8	networks	network	NOUN
fcis-7987	30	9	(	(	PUNCT
fcis-7987	30	10	siamese	siamese	NOUN
fcis-7987	30	11	-	-	PUNCT
fcis-7987	30	12	nn	nn	NOUN
fcis-7987	30	13	)	)	PUNCT
fcis-7987	30	14	to	to	PART
fcis-7987	30	15	address	address	VERB
fcis-7987	30	16	the	the	DET
fcis-7987	30	17	issue	issue	NOUN
fcis-7987	30	18	of	of	ADP
fcis-7987	30	19	class	class	NOUN
fcis-7987	30	20	imbalance	imbalance	NOUN
fcis-7987	30	21	in	in	ADP
fcis-7987	30	22	datasets	dataset	NOUN
fcis-7987	30	23	.	.	PUNCT
fcis-7987	31	1	while	while	SCONJ
fcis-7987	31	2	this	this	DET
fcis-7987	31	3	method	method	NOUN
fcis-7987	31	4	has	have	AUX
fcis-7987	31	5	made	make	VERB
fcis-7987	31	6	some	some	DET
fcis-7987	31	7	progress	progress	NOUN
fcis-7987	31	8	in	in	ADP
fcis-7987	31	9	detecting	detect	VERB
fcis-7987	31	10	minority	minority	NOUN
fcis-7987	31	11	class	class	NOUN
fcis-7987	31	12	anomalies	anomaly	NOUN
fcis-7987	31	13	,	,	PUNCT
fcis-7987	31	14	its	its	PRON
fcis-7987	31	15	precision	precision	NOUN
fcis-7987	31	16	still	still	ADV
fcis-7987	31	17	needs	need	VERB
fcis-7987	31	18	improvement	improvement	NOUN
fcis-7987	31	19	.	.	PUNCT
fcis-7987	32	1	li	li	PROPN
fcis-7987	32	2	chuan	chuan	PROPN
fcis-7987	33	1	[	[	X
fcis-7987	33	2	8	8	NUM
fcis-7987	33	3	]	]	PUNCT
fcis-7987	33	4	proposed	propose	VERB
fcis-7987	33	5	using	use	VERB
fcis-7987	33	6	generative	generative	ADJ
fcis-7987	33	7	adversarial	adversarial	ADJ
fcis-7987	33	8	networks	network	NOUN
fcis-7987	33	9	(	(	PUNCT
fcis-7987	33	10	gans	gan	NOUN
fcis-7987	33	11	)	)	PUNCT
fcis-7987	33	12	to	to	PART
fcis-7987	33	13	expand	expand	VERB
fcis-7987	33	14	the	the	DET
fcis-7987	33	15	number	number	NOUN
fcis-7987	33	16	of	of	ADP
fcis-7987	33	17	minority	minority	NOUN
fcis-7987	33	18	class	class	NOUN
fcis-7987	33	19	samples	sample	NOUN
fcis-7987	33	20	in	in	ADP
fcis-7987	33	21	the	the	DET
fcis-7987	33	22	data	datum	NOUN
fcis-7987	33	23	,	,	PUNCT
fcis-7987	33	24	comparing	compare	VERB
fcis-7987	33	25	the	the	DET
fcis-7987	33	26	expanded	expand	VERB
fcis-7987	33	27	data	datum	NOUN
fcis-7987	33	28	with	with	ADP
fcis-7987	33	29	the	the	DET
fcis-7987	33	30	original	original	ADJ
fcis-7987	33	31	data	datum	NOUN
fcis-7987	33	32	,	,	PUNCT
fcis-7987	33	33	and	and	CCONJ
fcis-7987	33	34	subsequently	subsequently	ADV
fcis-7987	33	35	conducting	conduct	VERB
fcis-7987	33	36	comparative	comparative	ADJ
fcis-7987	33	37	experiments	experiment	NOUN
fcis-7987	33	38	on	on	ADP
fcis-7987	33	39	the	the	DET
fcis-7987	33	40	original	original	ADJ
fcis-7987	33	41	and	and	CCONJ
fcis-7987	33	42	expanded	expand	VERB
fcis-7987	33	43	data	datum	NOUN
fcis-7987	33	44	using	use	VERB
fcis-7987	33	45	cnns	cnn	NOUN
fcis-7987	33	46	.	.	PUNCT
fcis-7987	34	1	the	the	DET
fcis-7987	34	2	experimental	experimental	ADJ
fcis-7987	34	3	results	result	NOUN
fcis-7987	34	4	showed	show	VERB
fcis-7987	34	5	that	that	SCONJ
fcis-7987	34	6	gans	gan	NOUN
fcis-7987	34	7	were	be	AUX
fcis-7987	34	8	effective	effective	ADJ
fcis-7987	34	9	in	in	ADP
fcis-7987	34	10	expanding	expand	VERB
fcis-7987	34	11	the	the	DET
fcis-7987	34	12	dataset	dataset	NOUN
fcis-7987	34	13	.	.	PUNCT
fcis-7987	35	1	the	the	DET
fcis-7987	35	2	fu	fu	NOUN
fcis-7987	36	1	[	[	X
fcis-7987	36	2	9	9	NUM
fcis-7987	36	3	]	]	PUNCT
fcis-7987	36	4	team	team	NOUN
fcis-7987	36	5	used	use	VERB
fcis-7987	36	6	an	an	DET
fcis-7987	36	7	adaptive	adaptive	ADJ
fcis-7987	36	8	synthetic	synthetic	ADJ
fcis-7987	36	9	sampling	sample	VERB
fcis-7987	36	10	algorithm	algorithm	NOUN
fcis-7987	36	11	to	to	PART
fcis-7987	36	12	expand	expand	VERB
fcis-7987	36	13	the	the	DET
fcis-7987	36	14	number	number	NOUN
fcis-7987	36	15	of	of	ADP
fcis-7987	36	16	minority	minority	NOUN
fcis-7987	36	17	class	class	NOUN
fcis-7987	36	18	samples	sample	NOUN
fcis-7987	36	19	to	to	PART
fcis-7987	36	20	address	address	VERB
fcis-7987	36	21	the	the	DET
fcis-7987	36	22	issue	issue	NOUN
fcis-7987	36	23	of	of	ADP
fcis-7987	36	24	data	datum	NOUN
fcis-7987	36	25	imbalance	imbalance	NOUN
fcis-7987	36	26	,	,	PUNCT
fcis-7987	36	27	but	but	CCONJ
fcis-7987	36	28	this	this	DET
fcis-7987	36	29	method	method	NOUN
fcis-7987	36	30	produced	produce	VERB
fcis-7987	36	31	a	a	DET
fcis-7987	36	32	large	large	ADJ
fcis-7987	36	33	amount	amount	NOUN
fcis-7987	36	34	of	of	ADP
fcis-7987	36	35	noise	noise	NOUN
fcis-7987	36	36	data	datum	NOUN
fcis-7987	36	37	due	due	ADP
fcis-7987	36	38	to	to	ADP
fcis-7987	36	39	the	the	DET
fcis-7987	36	40	influence	influence	NOUN
fcis-7987	36	41	of	of	ADP
fcis-7987	36	42	surrounding	surround	VERB
fcis-7987	36	43	data	datum	NOUN
fcis-7987	36	44	,	,	PUNCT
fcis-7987	36	45	ultimately	ultimately	ADV
fcis-7987	36	46	negatively	negatively	ADV
fcis-7987	36	47	impacting	impact	VERB
fcis-7987	36	48	the	the	DET
fcis-7987	36	49	model	model	NOUN
fcis-7987	36	50	's	's	PART
fcis-7987	36	51	performance	performance	NOUN
fcis-7987	36	52	.	.	PUNCT
fcis-7987	37	1	2	2	X
fcis-7987	37	2	.	.	X
fcis-7987	37	3	transformer_bilstm	transformer_bilstm	PROPN
fcis-7987	37	4	2.1	2.1	NUM
fcis-7987	37	5	.	.	PUNCT
fcis-7987	37	6	model	model	NOUN
fcis-7987	37	7	architecture	architecture	NOUN
fcis-7987	37	8	the	the	DET
fcis-7987	37	9	model	model	NOUN
fcis-7987	37	10	in	in	ADP
fcis-7987	37	11	this	this	DET
fcis-7987	37	12	paper	paper	NOUN
fcis-7987	37	13	first	first	ADV
fcis-7987	37	14	preprocesses	preprocesse	VERB
fcis-7987	37	15	the	the	DET
fcis-7987	37	16	initial	initial	ADJ
fcis-7987	37	17	data	datum	NOUN
fcis-7987	37	18	and	and	CCONJ
fcis-7987	37	19	inputs	input	VERB
fcis-7987	37	20	the	the	DET
fcis-7987	37	21	processed	process	VERB
fcis-7987	37	22	data	datum	NOUN
fcis-7987	37	23	into	into	ADP
fcis-7987	37	24	the	the	DET
fcis-7987	37	25	transformer	transformer	NOUN
fcis-7987	37	26	module	module	NOUN
fcis-7987	37	27	to	to	PART
fcis-7987	37	28	establish	establish	VERB
fcis-7987	37	29	connections	connection	NOUN
fcis-7987	37	30	between	between	ADP
fcis-7987	37	31	different	different	ADJ
fcis-7987	37	32	features	feature	NOUN
fcis-7987	37	33	,	,	PUNCT
fcis-7987	37	34	and	and	CCONJ
fcis-7987	37	35	extracts	extract	VERB
fcis-7987	37	36	richer	rich	ADJ
fcis-7987	37	37	feature	feature	NOUN
fcis-7987	37	38	information	information	NOUN
fcis-7987	37	39	through	through	ADP
fcis-7987	37	40	multi	multi	ADJ
fcis-7987	37	41	-	-	ADJ
fcis-7987	37	42	head	head	ADJ
fcis-7987	37	43	attention	attention	NOUN
fcis-7987	37	44	.	.	PUNCT
fcis-7987	38	1	then	then	ADV
fcis-7987	38	2	,	,	PUNCT
fcis-7987	38	3	the	the	DET
fcis-7987	38	4	data	data	NOUN
fcis-7987	38	5	is	be	AUX
fcis-7987	38	6	input	input	NOUN
fcis-7987	38	7	into	into	ADP
fcis-7987	38	8	a	a	DET
fcis-7987	38	9	bi	bi	ADJ
fcis-7987	38	10	-	-	ADJ
fcis-7987	38	11	lstm	lstm	ADJ
fcis-7987	38	12	neural	neural	ADJ
fcis-7987	38	13	network	network	NOUN
fcis-7987	38	14	to	to	PART
fcis-7987	38	15	obtain	obtain	VERB
fcis-7987	38	16	the	the	DET
fcis-7987	38	17	connection	connection	NOUN
fcis-7987	38	18	between	between	ADP
fcis-7987	38	19	the	the	DET
fcis-7987	38	20	previous	previous	ADJ
fcis-7987	38	21	and	and	CCONJ
fcis-7987	38	22	next	next	ADJ
fcis-7987	38	23	features	feature	NOUN
fcis-7987	38	24	to	to	PART
fcis-7987	38	25	retain	retain	VERB
fcis-7987	38	26	23	23	NUM
fcis-7987	38	27	its	its	PRON
fcis-7987	38	28	temporal	temporal	ADJ
fcis-7987	38	29	information	information	NOUN
fcis-7987	38	30	.	.	PUNCT
fcis-7987	39	1	finally	finally	ADV
fcis-7987	39	2	,	,	PUNCT
fcis-7987	39	3	features	feature	NOUN
fcis-7987	39	4	are	be	AUX
fcis-7987	39	5	further	far	ADV
fcis-7987	39	6	extracted	extract	VERB
fcis-7987	39	7	through	through	ADP
fcis-7987	39	8	dnn	dnn	PROPN
fcis-7987	39	9	and	and	CCONJ
fcis-7987	39	10	classified	classify	VERB
fcis-7987	39	11	and	and	CCONJ
fcis-7987	39	12	recognized	recognize	VERB
fcis-7987	39	13	using	use	VERB
fcis-7987	39	14	a	a	DET
fcis-7987	39	15	softmax	softmax	NOUN
fcis-7987	39	16	classifier	classifier	NOUN
fcis-7987	39	17	to	to	PART
fcis-7987	39	18	obtain	obtain	VERB
fcis-7987	39	19	the	the	DET
fcis-7987	39	20	results	result	NOUN
fcis-7987	39	21	.	.	PUNCT
fcis-7987	40	1	figure	figure	VERB
fcis-7987	40	2	1	1	NUM
fcis-7987	40	3	.	.	PUNCT
fcis-7987	41	1	tbl	tbl	NOUN
fcis-7987	41	2	intrusion	intrusion	NOUN
fcis-7987	41	3	detection	detection	NOUN
fcis-7987	41	4	model	model	NOUN
fcis-7987	41	5	2.2	2.2	NUM
fcis-7987	41	6	.	.	PUNCT
fcis-7987	42	1	transformer	transformer	NOUN
fcis-7987	42	2	-	-	PUNCT
fcis-7987	42	3	encoder	encoder	NOUN
fcis-7987	42	4	the	the	DET
fcis-7987	42	5	original	original	ADJ
fcis-7987	42	6	transformer	transformer	NOUN
fcis-7987	42	7	model	model	NOUN
fcis-7987	42	8	is	be	AUX
fcis-7987	42	9	divided	divide	VERB
fcis-7987	42	10	into	into	ADP
fcis-7987	42	11	two	two	NUM
fcis-7987	42	12	parts	part	NOUN
fcis-7987	42	13	:	:	PUNCT
fcis-7987	42	14	the	the	DET
fcis-7987	42	15	encoder	encoder	NOUN
fcis-7987	42	16	and	and	CCONJ
fcis-7987	42	17	the	the	DET
fcis-7987	42	18	decoder	decoder	NOUN
fcis-7987	42	19	.	.	PUNCT
fcis-7987	43	1	in	in	ADP
fcis-7987	43	2	this	this	DET
fcis-7987	43	3	chapter	chapter	NOUN
fcis-7987	43	4	,	,	PUNCT
fcis-7987	43	5	we	we	PRON
fcis-7987	43	6	only	only	ADV
fcis-7987	43	7	used	use	VERB
fcis-7987	43	8	the	the	DET
fcis-7987	43	9	encoder	encoder	NOUN
fcis-7987	43	10	module	module	NOUN
fcis-7987	43	11	of	of	ADP
fcis-7987	43	12	the	the	DET
fcis-7987	43	13	transformer	transformer	NOUN
fcis-7987	43	14	model	model	NOUN
fcis-7987	43	15	and	and	CCONJ
fcis-7987	43	16	made	make	VERB
fcis-7987	43	17	some	some	DET
fcis-7987	43	18	adjustments	adjustment	NOUN
fcis-7987	43	19	.	.	PUNCT
fcis-7987	44	1	the	the	DET
fcis-7987	44	2	encoder	encoder	NOUN
fcis-7987	44	3	is	be	AUX
fcis-7987	44	4	composed	compose	VERB
fcis-7987	44	5	of	of	ADP
fcis-7987	44	6	multiple	multiple	ADJ
fcis-7987	44	7	stacked	stack	VERB
fcis-7987	44	8	encoders	encoder	NOUN
fcis-7987	44	9	,	,	PUNCT
fcis-7987	44	10	each	each	PRON
fcis-7987	44	11	of	of	ADP
fcis-7987	44	12	which	which	PRON
fcis-7987	44	13	includes	include	VERB
fcis-7987	44	14	a	a	DET
fcis-7987	44	15	multi	multi	ADJ
fcis-7987	44	16	-	-	ADJ
fcis-7987	44	17	head	head	ADJ
fcis-7987	44	18	attention	attention	NOUN
fcis-7987	44	19	mechanism	mechanism	NOUN
fcis-7987	44	20	,	,	PUNCT
fcis-7987	44	21	a	a	DET
fcis-7987	44	22	feedforward	feedforward	ADJ
fcis-7987	44	23	neural	neural	ADJ
fcis-7987	44	24	network	network	NOUN
fcis-7987	44	25	,	,	PUNCT
fcis-7987	44	26	and	and	CCONJ
fcis-7987	44	27	a	a	DET
fcis-7987	44	28	residual	residual	ADJ
fcis-7987	44	29	network	network	NOUN
fcis-7987	44	30	.	.	PUNCT
fcis-7987	45	1	to	to	PART
fcis-7987	45	2	increase	increase	VERB
fcis-7987	45	3	training	training	NOUN
fcis-7987	45	4	speed	speed	NOUN
fcis-7987	45	5	and	and	CCONJ
fcis-7987	45	6	save	save	VERB
fcis-7987	45	7	time	time	NOUN
fcis-7987	45	8	,	,	PUNCT
fcis-7987	45	9	the	the	DET
fcis-7987	45	10	attention	attention	NOUN
fcis-7987	45	11	mechanism	mechanism	NOUN
fcis-7987	45	12	uses	use	VERB
fcis-7987	45	13	dot	dot	NOUN
fcis-7987	45	14	-	-	PUNCT
fcis-7987	45	15	product	product	NOUN
fcis-7987	45	16	attention	attention	NOUN
fcis-7987	45	17	that	that	PRON
fcis-7987	45	18	can	can	AUX
fcis-7987	45	19	be	be	AUX
fcis-7987	45	20	computed	compute	VERB
fcis-7987	45	21	in	in	ADP
fcis-7987	45	22	parallel	parallel	NOUN
fcis-7987	45	23	,	,	PUNCT
fcis-7987	45	24	with	with	ADP
fcis-7987	45	25	the	the	DET
fcis-7987	45	26	specific	specific	ADJ
fcis-7987	45	27	formula	formula	NOUN
fcis-7987	45	28	shown	show	VERB
fcis-7987	45	29	in	in	ADP
fcis-7987	45	30	equation	equation	NOUN
fcis-7987	45	31	(	(	PUNCT
fcis-7987	45	32	1	1	NUM
fcis-7987	45	33	)	)	PUNCT
fcis-7987	45	34	.	.	PUNCT
fcis-7987	46	1	attention(q	attention(q	PROPN
fcis-7987	46	2	,	,	PUNCT
fcis-7987	46	3	k	k	PROPN
fcis-7987	46	4	,	,	PUNCT
fcis-7987	46	5	v)=softmax	v)=softmax	NUM
fcis-7987	46	6	(	(	PUNCT
fcis-7987	46	7	q∙kt	q∙kt	PROPN
fcis-7987	46	8	√dk	√dk	PROPN
fcis-7987	46	9	)	)	PUNCT
fcis-7987	46	10	∙v	∙v	PROPN
fcis-7987	46	11	(	(	PUNCT
fcis-7987	46	12	1	1	X
fcis-7987	46	13	)	)	PUNCT
fcis-7987	46	14	the	the	DET
fcis-7987	46	15	feedforward	feedforward	ADJ
fcis-7987	46	16	neural	neural	ADJ
fcis-7987	46	17	network	network	NOUN
fcis-7987	46	18	has	have	VERB
fcis-7987	46	19	only	only	ADV
fcis-7987	46	20	one	one	NUM
fcis-7987	46	21	hidden	hide	VERB
fcis-7987	46	22	layer	layer	NOUN
fcis-7987	46	23	,	,	PUNCT
fcis-7987	46	24	which	which	PRON
fcis-7987	46	25	is	be	AUX
fcis-7987	46	26	a	a	DET
fcis-7987	46	27	perceptron	perceptron	NOUN
fcis-7987	46	28	with	with	ADP
fcis-7987	46	29	the	the	DET
fcis-7987	46	30	same	same	ADJ
fcis-7987	46	31	input	input	NOUN
fcis-7987	46	32	and	and	CCONJ
fcis-7987	46	33	output	output	NOUN
fcis-7987	46	34	dimensions	dimension	NOUN
fcis-7987	46	35	.	.	PUNCT
fcis-7987	47	1	since	since	SCONJ
fcis-7987	47	2	a	a	DET
fcis-7987	47	3	single	single	ADJ
fcis-7987	47	4	hidden	hide	VERB
fcis-7987	47	5	-	-	PUNCT
fcis-7987	47	6	layer	layer	NOUN
fcis-7987	47	7	network	network	NOUN
fcis-7987	47	8	has	have	VERB
fcis-7987	47	9	weak	weak	ADJ
fcis-7987	47	10	non	non	ADJ
fcis-7987	47	11	-	-	ADJ
fcis-7987	47	12	linear	linear	ADJ
fcis-7987	47	13	mapping	mapping	NOUN
fcis-7987	47	14	ability	ability	NOUN
fcis-7987	47	15	,	,	PUNCT
fcis-7987	47	16	and	and	CCONJ
fcis-7987	47	17	considering	consider	VERB
fcis-7987	47	18	the	the	DET
fcis-7987	47	19	balance	balance	NOUN
fcis-7987	47	20	between	between	ADP
fcis-7987	47	21	computational	computational	ADJ
fcis-7987	47	22	complexity	complexity	NOUN
fcis-7987	47	23	and	and	CCONJ
fcis-7987	47	24	mapping	mapping	NOUN
fcis-7987	47	25	ability	ability	NOUN
fcis-7987	47	26	,	,	PUNCT
fcis-7987	47	27	the	the	DET
fcis-7987	47	28	number	number	NOUN
fcis-7987	47	29	of	of	ADP
fcis-7987	47	30	hidden	hide	VERB
fcis-7987	47	31	layer	layer	NOUN
fcis-7987	47	32	neurons	neuron	NOUN
fcis-7987	47	33	is	be	AUX
fcis-7987	47	34	set	set	VERB
fcis-7987	47	35	to	to	ADP
fcis-7987	47	36	twice	twice	DET
fcis-7987	47	37	the	the	DET
fcis-7987	47	38	number	number	NOUN
fcis-7987	47	39	of	of	ADP
fcis-7987	47	40	input	input	NOUN
fcis-7987	47	41	layer	layer	NOUN
fcis-7987	47	42	neurons	neuron	NOUN
fcis-7987	47	43	in	in	ADP
fcis-7987	47	44	this	this	DET
fcis-7987	47	45	paper	paper	NOUN
fcis-7987	47	46	[	[	X
fcis-7987	47	47	10,11	10,11	NOUN
fcis-7987	47	48	]	]	X
fcis-7987	47	49	.	.	PUNCT
fcis-7987	48	1	the	the	DET
fcis-7987	48	2	activation	activation	NOUN
fcis-7987	48	3	function	function	NOUN
fcis-7987	48	4	relu	relu	NOUN
fcis-7987	48	5	is	be	AUX
fcis-7987	48	6	used	use	VERB
fcis-7987	48	7	.	.	PUNCT
fcis-7987	49	1	the	the	DET
fcis-7987	49	2	structure	structure	NOUN
fcis-7987	49	3	of	of	ADP
fcis-7987	49	4	the	the	DET
fcis-7987	49	5	entire	entire	ADJ
fcis-7987	49	6	transformer	transformer	NOUN
fcis-7987	49	7	-	-	PUNCT
fcis-7987	49	8	encoder	encoder	NOUN
fcis-7987	49	9	module	module	NOUN
fcis-7987	49	10	is	be	AUX
fcis-7987	49	11	shown	show	VERB
fcis-7987	49	12	in	in	ADP
fcis-7987	49	13	figure	figure	NOUN
fcis-7987	49	14	2	2	NUM
fcis-7987	49	15	.	.	PUNCT
fcis-7987	49	16	figure	figure	NOUN
fcis-7987	49	17	2	2	NUM
fcis-7987	49	18	.	.	PUNCT
fcis-7987	49	19	transfirmer	transfirmer	NOUN
fcis-7987	49	20	-	-	PUNCT
fcis-7987	49	21	encoder	encoder	NOUN
fcis-7987	49	22	2.3	2.3	NUM
fcis-7987	49	23	.	.	PUNCT
fcis-7987	50	1	bilstm	bilstm	NOUN
fcis-7987	50	2	the	the	DET
fcis-7987	50	3	long	long	ADJ
fcis-7987	50	4	short	short	ADJ
fcis-7987	50	5	-	-	PUNCT
fcis-7987	50	6	term	term	NOUN
fcis-7987	50	7	memory	memory	NOUN
fcis-7987	50	8	(	(	PUNCT
fcis-7987	50	9	lstm	lstm	ADJ
fcis-7987	50	10	)	)	PUNCT
fcis-7987	50	11	neural	neural	ADJ
fcis-7987	50	12	network	network	NOUN
fcis-7987	50	13	is	be	AUX
fcis-7987	50	14	a	a	DET
fcis-7987	50	15	type	type	NOUN
fcis-7987	50	16	of	of	ADP
fcis-7987	50	17	recurrent	recurrent	ADJ
fcis-7987	50	18	neural	neural	ADJ
fcis-7987	50	19	network	network	NOUN
fcis-7987	50	20	(	(	PUNCT
fcis-7987	50	21	rnn	rnn	PROPN
fcis-7987	50	22	)	)	PUNCT
fcis-7987	50	23	that	that	PRON
fcis-7987	50	24	solves	solve	VERB
fcis-7987	50	25	the	the	DET
fcis-7987	50	26	problem	problem	NOUN
fcis-7987	50	27	of	of	ADP
fcis-7987	50	28	long	long	ADJ
fcis-7987	50	29	-	-	PUNCT
fcis-7987	50	30	range	range	NOUN
fcis-7987	50	31	information	information	NOUN
fcis-7987	50	32	loss	loss	NOUN
fcis-7987	50	33	in	in	ADP
fcis-7987	50	34	long	long	ADJ
fcis-7987	50	35	sequences	sequence	NOUN
fcis-7987	50	36	.	.	PUNCT
fcis-7987	51	1	it	it	PRON
fcis-7987	51	2	is	be	AUX
fcis-7987	51	3	used	use	VERB
fcis-7987	51	4	for	for	ADP
fcis-7987	51	5	processing	process	VERB
fcis-7987	51	6	time	time	NOUN
fcis-7987	51	7	-	-	PUNCT
fcis-7987	51	8	series	series	NOUN
fcis-7987	51	9	information	information	NOUN
fcis-7987	51	10	and	and	CCONJ
fcis-7987	51	11	addresses	address	VERB
fcis-7987	51	12	the	the	DET
fcis-7987	51	13	issues	issue	NOUN
fcis-7987	51	14	of	of	ADP
fcis-7987	51	15	gradient	gradient	ADJ
fcis-7987	51	16	explosion	explosion	NOUN
fcis-7987	51	17	and	and	CCONJ
fcis-7987	51	18	vanishing	vanish	VERB
fcis-7987	51	19	in	in	ADP
fcis-7987	51	20	rnn	rnn	NOUN
fcis-7987	51	21	structures	structure	NOUN
fcis-7987	51	22	.	.	PUNCT
fcis-7987	52	1	it	it	PRON
fcis-7987	52	2	is	be	AUX
fcis-7987	52	3	capable	capable	ADJ
fcis-7987	52	4	of	of	ADP
fcis-7987	52	5	memorizing	memorize	VERB
fcis-7987	52	6	valuable	valuable	ADJ
fcis-7987	52	7	information	information	NOUN
fcis-7987	52	8	while	while	SCONJ
fcis-7987	52	9	discarding	discard	VERB
fcis-7987	52	10	redundant	redundant	ADJ
fcis-7987	52	11	memories	memory	NOUN
fcis-7987	52	12	[	[	X
fcis-7987	52	13	12	12	NUM
fcis-7987	52	14	]	]	PUNCT
fcis-7987	52	15	.	.	PUNCT
fcis-7987	53	1	the	the	DET
fcis-7987	53	2	bidirectional	bidirectional	ADJ
fcis-7987	53	3	lstm	lstm	NOUN
fcis-7987	53	4	consists	consist	VERB
fcis-7987	53	5	of	of	ADP
fcis-7987	53	6	a	a	DET
fcis-7987	53	7	forward	forward	ADJ
fcis-7987	53	8	lstm	lstm	NOUN
fcis-7987	53	9	and	and	CCONJ
fcis-7987	53	10	a	a	DET
fcis-7987	53	11	backward	backward	ADJ
fcis-7987	53	12	lstm	lstm	NOUN
fcis-7987	53	13	,	,	PUNCT
fcis-7987	53	14	which	which	PRON
fcis-7987	53	15	combine	combine	VERB
fcis-7987	53	16	to	to	PART
fcis-7987	53	17	contain	contain	VERB
fcis-7987	53	18	all	all	DET
fcis-7987	53	19	the	the	DET
fcis-7987	53	20	information	information	NOUN
fcis-7987	53	21	from	from	ADP
fcis-7987	53	22	both	both	DET
fcis-7987	53	23	directions	direction	NOUN
fcis-7987	53	24	.	.	PUNCT
fcis-7987	54	1	its	its	PRON
fcis-7987	54	2	structure	structure	NOUN
fcis-7987	54	3	is	be	AUX
fcis-7987	54	4	shown	show	VERB
fcis-7987	54	5	in	in	ADP
fcis-7987	54	6	figure	figure	NOUN
fcis-7987	54	7	3	3	NUM
fcis-7987	54	8	.	.	PUNCT
fcis-7987	54	9	figure	figure	VERB
fcis-7987	54	10	3	3	NUM
fcis-7987	54	11	.	.	PUNCT
fcis-7987	54	12	bilstm	bilstm	NOUN
fcis-7987	54	13	the	the	DET
fcis-7987	54	14	input	input	NOUN
fcis-7987	54	15	layer	layer	NOUN
fcis-7987	54	16	,	,	PUNCT
fcis-7987	54	17	input	input	NOUN
fcis-7987	54	18	,	,	PUNCT
fcis-7987	54	19	takes	take	VERB
fcis-7987	54	20	the	the	DET
fcis-7987	54	21	input	input	NOUN
fcis-7987	54	22	data	datum	NOUN
fcis-7987	54	23	and	and	CCONJ
fcis-7987	54	24	feeds	feed	VERB
fcis-7987	54	25	it	it	PRON
fcis-7987	54	26	into	into	ADP
fcis-7987	54	27	both	both	CCONJ
fcis-7987	54	28	the	the	DET
fcis-7987	54	29	forward	forward	ADJ
fcis-7987	54	30	network	network	NOUN
fcis-7987	54	31	,	,	PUNCT
fcis-7987	54	32	forward	forward	ADV
fcis-7987	54	33	,	,	PUNCT
fcis-7987	54	34	and	and	CCONJ
fcis-7987	54	35	the	the	DET
fcis-7987	54	36	backward	backward	ADJ
fcis-7987	54	37	network	network	NOUN
fcis-7987	54	38	,	,	PUNCT
fcis-7987	54	39	backward	backward	ADJ
fcis-7987	55	1	[	[	X
fcis-7987	55	2	13,14	13,14	NUM
fcis-7987	55	3	]	]	X
fcis-7987	55	4	.	.	PUNCT
fcis-7987	56	1	the	the	DET
fcis-7987	56	2	outputs	output	NOUN
fcis-7987	56	3	from	from	ADP
fcis-7987	56	4	these	these	DET
fcis-7987	56	5	networks	network	NOUN
fcis-7987	56	6	are	be	AUX
fcis-7987	56	7	concatenated	concatenate	VERB
fcis-7987	56	8	and	and	CCONJ
fcis-7987	56	9	represented	represent	VERB
fcis-7987	56	10	as	as	SCONJ
fcis-7987	56	11	follows	follow	VERB
fcis-7987	56	12	:	:	PUNCT
fcis-7987	56	13	hi=[hi	hi=[hi	PROPN
fcis-7987	56	14	⃗⃗	⃗⃗	PROPN
fcis-7987	56	15	,	,	PUNCT
fcis-7987	56	16	hi	hi	INTJ
fcis-7987	56	17	⃖⃗	⃖⃗	NOUN
fcis-7987	56	18	⃗	⃗	X
fcis-7987	56	19	]	]	X
fcis-7987	56	20	(	(	PUNCT
fcis-7987	56	21	2	2	NUM
fcis-7987	56	22	)	)	PUNCT
fcis-7987	56	23	here	here	ADV
fcis-7987	56	24	,	,	PUNCT
fcis-7987	56	25	hi	hi	INTJ
fcis-7987	56	26	represents	represent	VERB
fcis-7987	56	27	the	the	DET
fcis-7987	56	28	final	final	ADJ
fcis-7987	56	29	result	result	NOUN
fcis-7987	56	30	obtained	obtain	VERB
fcis-7987	56	31	by	by	ADP
fcis-7987	56	32	concatenating	concatenate	VERB
fcis-7987	56	33	the	the	DET
fcis-7987	56	34	outputs	output	NOUN
fcis-7987	56	35	of	of	ADP
fcis-7987	56	36	the	the	DET
fcis-7987	56	37	forward	forward	ADJ
fcis-7987	56	38	and	and	CCONJ
fcis-7987	56	39	backward	backward	ADJ
fcis-7987	56	40	networks	network	NOUN
fcis-7987	56	41	for	for	ADP
fcis-7987	56	42	the	the	DET
fcis-7987	56	43	i	i	PROPN
fcis-7987	56	44	-	-	PUNCT
fcis-7987	56	45	th	th	X
fcis-7987	56	46	input	input	NOUN
fcis-7987	56	47	,	,	PUNCT
fcis-7987	56	48	where	where	SCONJ
fcis-7987	56	49	i	i	PRON
fcis-7987	56	50	ranges	range	VERB
fcis-7987	56	51	from	from	ADP
fcis-7987	56	52	1	1	NUM
fcis-7987	56	53	to	to	ADP
fcis-7987	56	54	n	n	CCONJ
fcis-7987	56	55	,	,	PUNCT
fcis-7987	56	56	where	where	SCONJ
fcis-7987	56	57	n	n	X
fcis-7987	56	58	is	be	AUX
fcis-7987	56	59	the	the	DET
fcis-7987	56	60	total	total	ADJ
fcis-7987	56	61	number	number	NOUN
fcis-7987	56	62	of	of	ADP
fcis-7987	56	63	input	input	NOUN
fcis-7987	56	64	data	datum	NOUN
fcis-7987	56	65	.	.	PUNCT
fcis-7987	57	1	2.4	2.4	NUM
fcis-7987	57	2	.	.	PUNCT
fcis-7987	58	1	dnn	dnn	PROPN
fcis-7987	58	2	dnns	dnn	NOUN
fcis-7987	58	3	are	be	AUX
fcis-7987	58	4	generally	generally	ADV
fcis-7987	58	5	classified	classify	VERB
fcis-7987	58	6	based	base	VERB
fcis-7987	58	7	on	on	ADP
fcis-7987	58	8	the	the	DET
fcis-7987	58	9	different	different	ADJ
fcis-7987	58	10	layers	layer	NOUN
fcis-7987	58	11	,	,	PUNCT
fcis-7987	58	12	which	which	PRON
fcis-7987	58	13	can	can	AUX
fcis-7987	58	14	be	be	AUX
fcis-7987	58	15	divided	divide	VERB
fcis-7987	58	16	into	into	ADP
fcis-7987	58	17	three	three	NUM
fcis-7987	58	18	categories	category	NOUN
fcis-7987	58	19	:	:	PUNCT
fcis-7987	58	20	input	input	NOUN
fcis-7987	58	21	layer	layer	NOUN
fcis-7987	58	22	,	,	PUNCT
fcis-7987	58	23	hidden	hide	VERB
fcis-7987	58	24	layer	layer	NOUN
fcis-7987	58	25	,	,	PUNCT
fcis-7987	58	26	and	and	CCONJ
fcis-7987	58	27	output	output	NOUN
fcis-7987	58	28	layer	layer	NOUN
fcis-7987	58	29	.	.	PUNCT
fcis-7987	59	1	typically	typically	ADV
fcis-7987	59	2	,	,	PUNCT
fcis-7987	59	3	the	the	DET
fcis-7987	59	4	first	first	ADJ
fcis-7987	59	5	layer	layer	NOUN
fcis-7987	59	6	is	be	AUX
fcis-7987	59	7	the	the	DET
fcis-7987	59	8	input	input	NOUN
fcis-7987	59	9	layer	layer	NOUN
fcis-7987	59	10	,	,	PUNCT
fcis-7987	59	11	the	the	DET
fcis-7987	59	12	last	last	ADJ
fcis-7987	59	13	layer	layer	NOUN
fcis-7987	59	14	is	be	AUX
fcis-7987	59	15	the	the	DET
fcis-7987	59	16	output	output	NOUN
fcis-7987	59	17	layer	layer	NOUN
fcis-7987	59	18	,	,	PUNCT
fcis-7987	59	19	and	and	CCONJ
fcis-7987	59	20	all	all	DET
fcis-7987	59	21	layers	layer	NOUN
fcis-7987	59	22	in	in	ADP
fcis-7987	59	23	between	between	ADP
fcis-7987	59	24	are	be	AUX
fcis-7987	59	25	hidden	hidden	ADJ
fcis-7987	59	26	layers	layer	NOUN
fcis-7987	59	27	.	.	PUNCT
fcis-7987	60	1	the	the	DET
fcis-7987	60	2	network	network	NOUN
fcis-7987	60	3	structure	structure	NOUN
fcis-7987	60	4	of	of	ADP
fcis-7987	60	5	dnn	dnn	PROPN
fcis-7987	60	6	is	be	AUX
fcis-7987	60	7	shown	show	VERB
fcis-7987	60	8	in	in	ADP
fcis-7987	60	9	figure	figure	NOUN
fcis-7987	60	10	4	4	NUM
fcis-7987	60	11	.	.	PUNCT
fcis-7987	60	12	figure	figure	VERB
fcis-7987	60	13	4	4	NUM
fcis-7987	60	14	.	.	PUNCT
fcis-7987	61	1	dnn	dnn	PROPN
fcis-7987	61	2	the	the	DET
fcis-7987	61	3	general	general	ADJ
fcis-7987	61	4	calculation	calculation	NOUN
fcis-7987	61	5	formula	formula	NOUN
fcis-7987	61	6	(	(	PUNCT
fcis-7987	61	7	3	3	NUM
fcis-7987	61	8	)	)	PUNCT
fcis-7987	61	9	for	for	ADP
fcis-7987	61	10	dnn	dnn	PROPN
fcis-7987	61	11	is	be	AUX
fcis-7987	61	12	as	as	SCONJ
fcis-7987	61	13	follows	follow	VERB
fcis-7987	61	14	:	:	PUNCT
fcis-7987	61	15	f(x)=	f(x)=	NOUN
fcis-7987	61	16	σ(wx+b	σ(wx+b	PROPN
fcis-7987	61	17	)	)	PUNCT
fcis-7987	61	18	(	(	PUNCT
fcis-7987	61	19	3	3	X
fcis-7987	61	20	)	)	PUNCT
fcis-7987	61	21	3	3	NUM
fcis-7987	61	22	.	.	NOUN
fcis-7987	61	23	results	result	NOUN
fcis-7987	61	24	and	and	CCONJ
fcis-7987	61	25	analysis	analysis	NOUN
fcis-7987	61	26	3.1	3.1	NUM
fcis-7987	61	27	.	.	PUNCT
fcis-7987	62	1	environment	environment	NOUN
fcis-7987	62	2	the	the	DET
fcis-7987	62	3	intrusion	intrusion	NOUN
fcis-7987	62	4	detection	detection	NOUN
fcis-7987	62	5	model	model	NOUN
fcis-7987	62	6	based	base	VERB
fcis-7987	62	7	on	on	ADP
fcis-7987	62	8	tbl	tbl	NOUN
fcis-7987	62	9	uses	use	VERB
fcis-7987	62	10	a	a	DET
fcis-7987	62	11	windows	window	NOUN
fcis-7987	62	12	operating	operating	NOUN
fcis-7987	62	13	system	system	NOUN
fcis-7987	62	14	,	,	PUNCT
fcis-7987	62	15	an	an	DET
fcis-7987	62	16	intel(r	intel(r	NOUN
fcis-7987	62	17	)	)	PUNCT
fcis-7987	62	18	core	core	NOUN
fcis-7987	62	19	(	(	PUNCT
fcis-7987	62	20	tm	tm	NOUN
fcis-7987	62	21	)	)	PUNCT
fcis-7987	62	22	i7	i7	ADJ
fcis-7987	62	23	-	-	PUNCT
fcis-7987	62	24	11700h	11700h	NUM
fcis-7987	62	25	cpu	cpu	NOUN
fcis-7987	62	26	@	@	ADP
fcis-7987	62	27	2.50ghz	2.50ghz	NUM
fcis-7987	62	28	processor	processor	NOUN
fcis-7987	62	29	,	,	PUNCT
fcis-7987	62	30	and	and	CCONJ
fcis-7987	62	31	16	16	NUM
fcis-7987	62	32	gb	gb	NOUN
fcis-7987	62	33	of	of	ADP
fcis-7987	62	34	memory	memory	NOUN
fcis-7987	62	35	.	.	PUNCT
fcis-7987	63	1	the	the	DET
fcis-7987	63	2	model	model	NOUN
fcis-7987	63	3	training	training	NOUN
fcis-7987	63	4	is	be	AUX
fcis-7987	63	5	accelerated	accelerate	VERB
fcis-7987	63	6	using	use	VERB
fcis-7987	63	7	the	the	DET
fcis-7987	63	8	nivdia	nivdia	PROPN
fcis-7987	63	9	geforce	geforce	PROPN
fcis-7987	63	10	rtx	rtx	PROPN
fcis-7987	63	11	3060ti	3060ti	PROPN
fcis-7987	63	12	gpu	gpu	PROPN
fcis-7987	63	13	,	,	PUNCT
fcis-7987	63	14	and	and	CCONJ
fcis-7987	63	15	the	the	DET
fcis-7987	63	16	programming	programming	NOUN
fcis-7987	63	17	tools	tool	NOUN
fcis-7987	63	18	used	use	VERB
fcis-7987	63	19	are	be	AUX
fcis-7987	63	20	pytorch	pytorch	NOUN
fcis-7987	63	21	,	,	PUNCT
fcis-7987	63	22	cuda11.0	cuda11.0	NOUN
fcis-7987	63	23	,	,	PUNCT
fcis-7987	63	24	and	and	CCONJ
fcis-7987	63	25	python	python	NOUN
fcis-7987	63	26	3.6	3.6	NUM
fcis-7987	63	27	.	.	PUNCT
fcis-7987	64	1	3.2	3.2	NUM
fcis-7987	64	2	.	.	PUNCT
fcis-7987	65	1	dataset	dataset	VERB
fcis-7987	65	2	the	the	DET
fcis-7987	65	3	dataset	dataset	NOUN
fcis-7987	65	4	used	use	VERB
fcis-7987	65	5	in	in	ADP
fcis-7987	65	6	this	this	DET
fcis-7987	65	7	article	article	NOUN
fcis-7987	65	8	's	's	PART
fcis-7987	65	9	experiments	experiment	NOUN
fcis-7987	65	10	is	be	AUX
fcis-7987	65	11	the	the	DET
fcis-7987	65	12	nslkdd	nslkdd	ADJ
fcis-7987	65	13	dataset	dataset	NOUN
fcis-7987	65	14	,	,	PUNCT
fcis-7987	65	15	which	which	PRON
fcis-7987	65	16	is	be	AUX
fcis-7987	65	17	a	a	DET
fcis-7987	65	18	subset	subset	NOUN
fcis-7987	65	19	of	of	ADP
fcis-7987	65	20	the	the	DET
fcis-7987	65	21	classic	classic	ADJ
fcis-7987	65	22	kdd99	kdd99	NOUN
fcis-7987	65	23	dataset	dataset	VERB
fcis-7987	65	24	with	with	ADP
fcis-7987	65	25	an	an	DET
fcis-7987	65	26	additional	additional	ADJ
fcis-7987	65	27	feature	feature	NOUN
fcis-7987	65	28	.	.	PUNCT
fcis-7987	66	1	to	to	PART
fcis-7987	66	2	avoid	avoid	VERB
fcis-7987	66	3	the	the	DET
fcis-7987	66	4	classifier	classifier	NOUN
fcis-7987	66	5	being	be	AUX
fcis-7987	66	6	biased	bias	VERB
fcis-7987	66	7	towards	towards	ADP
fcis-7987	66	8	frequent	frequent	ADJ
fcis-7987	66	9	records	record	NOUN
fcis-7987	66	10	and	and	CCONJ
fcis-7987	66	11	to	to	PART
fcis-7987	66	12	ensure	ensure	VERB
fcis-7987	66	13	accurate	accurate	ADJ
fcis-7987	66	14	detection	detection	NOUN
fcis-7987	66	15	,	,	PUNCT
fcis-7987	66	16	the	the	DET
fcis-7987	66	17	nsl	nsl	NOUN
fcis-7987	66	18	-	-	PUNCT
fcis-7987	66	19	kdd	kdd	PROPN
fcis-7987	66	20	dataset	dataset	NOUN
fcis-7987	66	21	removes	remove	VERB
fcis-7987	66	22	a	a	DET
fcis-7987	66	23	large	large	ADJ
fcis-7987	66	24	amount	amount	NOUN
fcis-7987	66	25	of	of	ADP
fcis-7987	66	26	redundant	redundant	ADJ
fcis-7987	66	27	and	and	CCONJ
fcis-7987	66	28	duplicated	duplicate	VERB
fcis-7987	66	29	data	datum	NOUN
fcis-7987	66	30	from	from	ADP
fcis-7987	66	31	the	the	DET
fcis-7987	66	32	kdd99	kdd99	NOUN
fcis-7987	66	33	dataset	dataset	NOUN
fcis-7987	66	34	and	and	CCONJ
fcis-7987	66	35	reasonably	reasonably	ADV
fcis-7987	66	36	splits	split	VERB
fcis-7987	66	37	it	it	PRON
fcis-7987	66	38	into	into	ADP
fcis-7987	66	39	training	training	NOUN
fcis-7987	66	40	and	and	CCONJ
fcis-7987	66	41	testing	testing	NOUN
fcis-7987	66	42	sets	set	NOUN
fcis-7987	66	43	.	.	PUNCT
fcis-7987	67	1	24	24	NUM
fcis-7987	67	2	each	each	DET
fcis-7987	67	3	record	record	NOUN
fcis-7987	67	4	in	in	ADP
fcis-7987	67	5	the	the	DET
fcis-7987	67	6	nsl	nsl	PROPN
fcis-7987	67	7	-	-	PUNCT
fcis-7987	67	8	kdd	kdd	PROPN
fcis-7987	67	9	dataset	dataset	NOUN
fcis-7987	67	10	contains	contain	VERB
fcis-7987	67	11	43	43	NUM
fcis-7987	67	12	features	feature	NOUN
fcis-7987	67	13	,	,	PUNCT
fcis-7987	67	14	where	where	SCONJ
fcis-7987	67	15	the	the	DET
fcis-7987	67	16	first	first	ADJ
fcis-7987	67	17	41	41	NUM
fcis-7987	67	18	features	feature	NOUN
fcis-7987	67	19	represent	represent	VERB
fcis-7987	67	20	the	the	DET
fcis-7987	67	21	traffic	traffic	NOUN
fcis-7987	67	22	itself	itself	PRON
fcis-7987	67	23	,	,	PUNCT
fcis-7987	67	24	the	the	DET
fcis-7987	67	25	42nd	42nd	ADJ
fcis-7987	67	26	feature	feature	NOUN
fcis-7987	67	27	is	be	AUX
fcis-7987	67	28	the	the	DET
fcis-7987	67	29	label	label	NOUN
fcis-7987	67	30	indicating	indicate	VERB
fcis-7987	67	31	whether	whether	SCONJ
fcis-7987	67	32	the	the	DET
fcis-7987	67	33	record	record	NOUN
fcis-7987	67	34	is	be	AUX
fcis-7987	67	35	normal	normal	ADJ
fcis-7987	67	36	or	or	CCONJ
fcis-7987	67	37	an	an	DET
fcis-7987	67	38	attack	attack	NOUN
fcis-7987	67	39	behavior	behavior	NOUN
fcis-7987	67	40	,	,	PUNCT
fcis-7987	67	41	and	and	CCONJ
fcis-7987	67	42	the	the	DET
fcis-7987	67	43	43rd	43rd	ADJ
fcis-7987	67	44	feature	feature	NOUN
fcis-7987	67	45	is	be	AUX
fcis-7987	67	46	an	an	DET
fcis-7987	67	47	additional	additional	ADJ
fcis-7987	67	48	feature	feature	NOUN
fcis-7987	67	49	added	add	VERB
fcis-7987	67	50	by	by	ADP
fcis-7987	67	51	nsl	nsl	PROPN
fcis-7987	67	52	-	-	PUNCT
fcis-7987	67	53	kdd	kdd	PROPN
fcis-7987	67	54	on	on	ADP
fcis-7987	67	55	the	the	DET
fcis-7987	67	56	kdd99	kdd99	NOUN
fcis-7987	67	57	dataset	dataset	NOUN
fcis-7987	67	58	,	,	PUNCT
fcis-7987	67	59	representing	represent	VERB
fcis-7987	67	60	the	the	DET
fcis-7987	67	61	difficulty	difficulty	NOUN
fcis-7987	67	62	score	score	NOUN
fcis-7987	67	63	of	of	ADP
fcis-7987	67	64	detecting	detect	VERB
fcis-7987	67	65	the	the	DET
fcis-7987	67	66	record	record	NOUN
fcis-7987	67	67	.	.	PUNCT
fcis-7987	68	1	the	the	DET
fcis-7987	68	2	dataset	dataset	NOUN
fcis-7987	68	3	contains	contain	VERB
fcis-7987	68	4	a	a	DET
fcis-7987	68	5	total	total	NOUN
fcis-7987	68	6	of	of	ADP
fcis-7987	68	7	5	5	NUM
fcis-7987	68	8	classes	class	NOUN
fcis-7987	68	9	of	of	ADP
fcis-7987	68	10	data	datum	NOUN
fcis-7987	68	11	,	,	PUNCT
fcis-7987	68	12	including	include	VERB
fcis-7987	68	13	4	4	NUM
fcis-7987	68	14	types	type	NOUN
fcis-7987	68	15	of	of	ADP
fcis-7987	68	16	attack	attack	NOUN
fcis-7987	68	17	and	and	CCONJ
fcis-7987	68	18	1	1	NUM
fcis-7987	68	19	type	type	NOUN
fcis-7987	68	20	of	of	ADP
fcis-7987	68	21	normal	normal	ADJ
fcis-7987	68	22	data	datum	NOUN
fcis-7987	68	23	.	.	PUNCT
fcis-7987	69	1	the	the	DET
fcis-7987	69	2	four	four	NUM
fcis-7987	69	3	types	type	NOUN
fcis-7987	69	4	of	of	ADP
fcis-7987	69	5	attack	attack	NOUN
fcis-7987	69	6	are	be	AUX
fcis-7987	69	7	:	:	PUNCT
fcis-7987	69	8	dos	do	NOUN
fcis-7987	69	9	,	,	PUNCT
fcis-7987	69	10	probe	probe	NOUN
fcis-7987	69	11	,	,	PUNCT
fcis-7987	69	12	u2r	u2r	NOUN
fcis-7987	69	13	,	,	PUNCT
fcis-7987	69	14	r2l	r2l	NOUN
fcis-7987	69	15	.	.	PUNCT
fcis-7987	70	1	these	these	DET
fcis-7987	70	2	4	4	NUM
fcis-7987	70	3	types	type	NOUN
fcis-7987	70	4	of	of	ADP
fcis-7987	70	5	attacks	attack	NOUN
fcis-7987	70	6	are	be	AUX
fcis-7987	70	7	further	far	ADV
fcis-7987	70	8	divided	divide	VERB
fcis-7987	70	9	into	into	ADP
fcis-7987	70	10	38	38	NUM
fcis-7987	70	11	attack	attack	NOUN
fcis-7987	70	12	methods	method	NOUN
fcis-7987	70	13	,	,	PUNCT
fcis-7987	70	14	with	with	ADP
fcis-7987	70	15	22	22	NUM
fcis-7987	70	16	types	type	NOUN
fcis-7987	70	17	of	of	ADP
fcis-7987	70	18	attacks	attack	NOUN
fcis-7987	70	19	included	include	VERB
fcis-7987	70	20	in	in	ADP
fcis-7987	70	21	the	the	DET
fcis-7987	70	22	training	training	NOUN
fcis-7987	70	23	set	set	NOUN
fcis-7987	70	24	and	and	CCONJ
fcis-7987	70	25	the	the	DET
fcis-7987	70	26	remaining	remain	VERB
fcis-7987	70	27	attack	attack	NOUN
fcis-7987	70	28	types	type	NOUN
fcis-7987	70	29	included	include	VERB
fcis-7987	70	30	in	in	ADP
fcis-7987	70	31	the	the	DET
fcis-7987	70	32	testing	testing	NOUN
fcis-7987	70	33	set	set	NOUN
fcis-7987	70	34	.	.	PUNCT
fcis-7987	71	1	the	the	DET
fcis-7987	71	2	generalization	generalization	NOUN
fcis-7987	71	3	ability	ability	NOUN
fcis-7987	71	4	of	of	ADP
fcis-7987	71	5	the	the	DET
fcis-7987	71	6	model	model	NOUN
fcis-7987	71	7	is	be	AUX
fcis-7987	71	8	evaluated	evaluate	VERB
fcis-7987	71	9	by	by	ADP
fcis-7987	71	10	testing	test	VERB
fcis-7987	71	11	whether	whether	SCONJ
fcis-7987	71	12	it	it	PRON
fcis-7987	71	13	can	can	AUX
fcis-7987	71	14	detect	detect	VERB
fcis-7987	71	15	unknown	unknown	ADJ
fcis-7987	71	16	attack	attack	NOUN
fcis-7987	71	17	types	type	NOUN
fcis-7987	71	18	in	in	ADP
fcis-7987	71	19	the	the	DET
fcis-7987	71	20	testing	testing	NOUN
fcis-7987	71	21	set	set	NOUN
fcis-7987	71	22	.	.	PUNCT
fcis-7987	72	1	the	the	DET
fcis-7987	72	2	division	division	NOUN
fcis-7987	72	3	of	of	ADP
fcis-7987	72	4	attack	attack	NOUN
fcis-7987	72	5	types	type	NOUN
fcis-7987	72	6	in	in	ADP
fcis-7987	72	7	the	the	DET
fcis-7987	72	8	training	training	NOUN
fcis-7987	72	9	and	and	CCONJ
fcis-7987	72	10	testing	testing	NOUN
fcis-7987	72	11	sets	set	NOUN
fcis-7987	72	12	is	be	AUX
fcis-7987	72	13	shown	show	VERB
fcis-7987	72	14	in	in	ADP
fcis-7987	72	15	table	table	NOUN
fcis-7987	72	16	1	1	NUM
fcis-7987	72	17	.	.	PUNCT
fcis-7987	72	18	table	table	NOUN
fcis-7987	72	19	1	1	NUM
fcis-7987	72	20	.	.	PUNCT
fcis-7987	73	1	division	division	NOUN
fcis-7987	73	2	of	of	ADP
fcis-7987	73	3	attack	attack	NOUN
fcis-7987	73	4	types	type	NOUN
fcis-7987	73	5	in	in	ADP
fcis-7987	73	6	subsets	subset	NOUN
fcis-7987	73	7	type	type	NOUN
fcis-7987	73	8	attack	attack	NOUN
fcis-7987	73	9	types	type	NOUN
fcis-7987	73	10	in	in	ADP
fcis-7987	73	11	the	the	DET
fcis-7987	73	12	training	training	NOUN
fcis-7987	73	13	set	set	VERB
fcis-7987	73	14	attack	attack	NOUN
fcis-7987	73	15	types	type	NOUN
fcis-7987	73	16	in	in	ADP
fcis-7987	73	17	the	the	DET
fcis-7987	73	18	test	test	NOUN
fcis-7987	73	19	set	set	VERB
fcis-7987	73	20	dos	do	NOUN
fcis-7987	73	21	back	back	ADP
fcis-7987	73	22	,	,	PUNCT
fcis-7987	73	23	land	land	NOUN
fcis-7987	73	24	,	,	PUNCT
fcis-7987	73	25	neptune	neptune	NOUN
fcis-7987	73	26	,	,	PUNCT
fcis-7987	73	27	pod	pod	NOUN
fcis-7987	73	28	,	,	PUNCT
fcis-7987	73	29	smurf	smurf	PROPN
fcis-7987	73	30	,	,	PUNCT
fcis-7987	73	31	teardrop	teardrop	NOUN
fcis-7987	73	32	apache2	apache2	PROPN
fcis-7987	73	33	,	,	PUNCT
fcis-7987	73	34	mailbomp	mailbomp	ADJ
fcis-7987	73	35	,	,	PUNCT
fcis-7987	73	36	processtable	processtable	ADJ
fcis-7987	73	37	,	,	PUNCT
fcis-7987	73	38	udpstorm	udpstorm	NOUN
fcis-7987	73	39	,	,	PUNCT
fcis-7987	73	40	worm	worm	NOUN
fcis-7987	73	41	probe	probe	NOUN
fcis-7987	73	42	ipsweep	ipsweep	NOUN
fcis-7987	73	43	,	,	PUNCT
fcis-7987	73	44	nmap	nmap	ADV
fcis-7987	73	45	,	,	PUNCT
fcis-7987	73	46	portsweep	portsweep	NOUN
fcis-7987	73	47	,	,	PUNCT
fcis-7987	73	48	satan	satan	PROPN
fcis-7987	73	49	mscan	mscan	PROPN
fcis-7987	73	50	,	,	PUNCT
fcis-7987	73	51	saint	saint	PROPN
fcis-7987	73	52	u2r	u2r	PROPN
fcis-7987	73	53	buffer_overflow	buffer_overflow	PROPN
fcis-7987	73	54	,	,	PUNCT
fcis-7987	73	55	loadmodule	loadmodule	NOUN
fcis-7987	73	56	,	,	PUNCT
fcis-7987	73	57	perl	perl	NOUN
fcis-7987	73	58	,	,	PUNCT
fcis-7987	73	59	rootkit	rootkit	NOUN
fcis-7987	73	60	ps	ps	NOUN
fcis-7987	73	61	,	,	PUNCT
fcis-7987	73	62	sqlattack	sqlattack	NOUN
fcis-7987	73	63	,	,	PUNCT
fcis-7987	73	64	xterm	xterm	PROPN
fcis-7987	73	65	r2l	r2l	NOUN
fcis-7987	73	66	ftp_write	ftp_write	NOUN
fcis-7987	73	67	,	,	PUNCT
fcis-7987	73	68	guess_passwd	guess_passwd	PROPN
fcis-7987	73	69	,	,	PUNCT
fcis-7987	73	70	imap	imap	NOUN
fcis-7987	73	71	,	,	PUNCT
fcis-7987	73	72	multihop	multihop	NOUN
fcis-7987	73	73	,	,	PUNCT
fcis-7987	73	74	phf	phf	NOUN
fcis-7987	73	75	,	,	PUNCT
fcis-7987	73	76	spy	spy	NOUN
fcis-7987	73	77	,	,	PUNCT
fcis-7987	73	78	warezmaster	warezmaster	NOUN
fcis-7987	73	79	named	name	VERB
fcis-7987	73	80	,	,	PUNCT
fcis-7987	73	81	httpummel	httpummel	NOUN
fcis-7987	73	82	,	,	PUNCT
fcis-7987	73	83	sendmail	sendmail	NOUN
fcis-7987	73	84	,	,	PUNCT
fcis-7987	73	85	snmpgetattac	snmpgetattac	NOUN
fcis-7987	73	86	,	,	PUNCT
fcis-7987	73	87	snmpguess	snmpguess	NOUN
fcis-7987	73	88	,	,	PUNCT
fcis-7987	73	89	xlock	xlock	PROPN
fcis-7987	73	90	,	,	PUNCT
fcis-7987	73	91	snaoop	snaoop	VERB
fcis-7987	73	92	the	the	DET
fcis-7987	73	93	nsl	nsl	NOUN
fcis-7987	73	94	-	-	PUNCT
fcis-7987	73	95	kdd	kdd	PROPN
fcis-7987	73	96	dataset	dataset	NOUN
fcis-7987	73	97	is	be	AUX
fcis-7987	73	98	divided	divide	VERB
fcis-7987	73	99	into	into	ADP
fcis-7987	73	100	four	four	NUM
fcis-7987	73	101	subsets	subset	NOUN
fcis-7987	73	102	:	:	PUNCT
fcis-7987	73	103	kddtest+	kddtest+	NOUN
fcis-7987	73	104	,	,	PUNCT
fcis-7987	73	105	kddtest-21	kddtest-21	PROPN
fcis-7987	73	106	,	,	PUNCT
fcis-7987	73	107	kddtrain+	kddtrain+	ADJ
fcis-7987	73	108	,	,	PUNCT
fcis-7987	73	109	and	and	CCONJ
fcis-7987	73	110	kddtrain+20	kddtrain+20	NOUN
fcis-7987	73	111	%	%	NOUN
fcis-7987	73	112	.	.	PUNCT
fcis-7987	74	1	kddtest-21	kddtest-21	PROPN
fcis-7987	74	2	is	be	AUX
fcis-7987	74	3	a	a	DET
fcis-7987	74	4	subset	subset	NOUN
fcis-7987	74	5	of	of	ADP
fcis-7987	74	6	kddtest+	kddtest+	NOUN
fcis-7987	74	7	that	that	PRON
fcis-7987	74	8	removes	remove	VERB
fcis-7987	74	9	data	datum	NOUN
fcis-7987	74	10	with	with	ADP
fcis-7987	74	11	the	the	DET
fcis-7987	74	12	43rd	43rd	ADJ
fcis-7987	74	13	feature	feature	NOUN
fcis-7987	74	14	equal	equal	ADJ
fcis-7987	74	15	to	to	ADP
fcis-7987	74	16	21	21	NUM
fcis-7987	74	17	,	,	PUNCT
fcis-7987	74	18	which	which	PRON
fcis-7987	74	19	represents	represent	VERB
fcis-7987	74	20	easily	easily	ADV
fcis-7987	74	21	detectable	detectable	ADJ
fcis-7987	74	22	intrusion	intrusion	NOUN
fcis-7987	74	23	data	datum	NOUN
fcis-7987	74	24	.	.	PUNCT
fcis-7987	75	1	therefore	therefore	ADV
fcis-7987	75	2	,	,	PUNCT
fcis-7987	75	3	the	the	DET
fcis-7987	75	4	kddtest-21	kddtest-21	PROPN
fcis-7987	75	5	dataset	dataset	NOUN
fcis-7987	75	6	consists	consist	NOUN
fcis-7987	75	7	of	of	ADP
fcis-7987	75	8	difficult	difficult	ADJ
fcis-7987	75	9	-	-	PUNCT
fcis-7987	75	10	to	to	ADP
fcis-7987	75	11	-	-	PUNCT
fcis-7987	75	12	detect	detect	VERB
fcis-7987	75	13	traffic	traffic	NOUN
fcis-7987	75	14	data	datum	NOUN
fcis-7987	75	15	.	.	PUNCT
fcis-7987	76	1	kddtrain+20	kddtrain+20	NOUN
fcis-7987	76	2	%	%	NOUN
fcis-7987	76	3	is	be	AUX
fcis-7987	76	4	a	a	DET
fcis-7987	76	5	subset	subset	NOUN
fcis-7987	76	6	of	of	ADP
fcis-7987	76	7	kddtrain+	kddtrain+	PROPN
fcis-7987	76	8	with	with	ADP
fcis-7987	76	9	only	only	ADV
fcis-7987	76	10	20	20	NUM
fcis-7987	76	11	%	%	NOUN
fcis-7987	76	12	of	of	ADP
fcis-7987	76	13	its	its	PRON
fcis-7987	76	14	data	datum	NOUN
fcis-7987	76	15	.	.	PUNCT
fcis-7987	77	1	table	table	NOUN
fcis-7987	77	2	2	2	NUM
fcis-7987	77	3	shows	show	VERB
fcis-7987	77	4	the	the	DET
fcis-7987	77	5	number	number	NOUN
fcis-7987	77	6	of	of	ADP
fcis-7987	77	7	instances	instance	NOUN
fcis-7987	77	8	and	and	CCONJ
fcis-7987	77	9	the	the	DET
fcis-7987	77	10	proportion	proportion	NOUN
fcis-7987	77	11	of	of	ADP
fcis-7987	77	12	each	each	DET
fcis-7987	77	13	data	datum	NOUN
fcis-7987	77	14	type	type	NOUN
fcis-7987	77	15	in	in	ADP
fcis-7987	77	16	each	each	DET
fcis-7987	77	17	subset	subset	NOUN
fcis-7987	77	18	of	of	ADP
fcis-7987	77	19	the	the	DET
fcis-7987	77	20	nsl	nsl	PROPN
fcis-7987	77	21	-	-	PUNCT
fcis-7987	77	22	kdd	kdd	PROPN
fcis-7987	77	23	dataset	dataset	NOUN
fcis-7987	77	24	.	.	PUNCT
fcis-7987	78	1	table	table	NOUN
fcis-7987	78	2	2	2	NUM
fcis-7987	78	3	.	.	PUNCT
fcis-7987	78	4	quantity	quantity	NOUN
fcis-7987	78	5	and	and	CCONJ
fcis-7987	78	6	proportion	proportion	NOUN
fcis-7987	78	7	of	of	ADP
fcis-7987	78	8	various	various	ADJ
fcis-7987	78	9	types	type	NOUN
fcis-7987	78	10	of	of	ADP
fcis-7987	78	11	data	datum	NOUN
fcis-7987	78	12	in	in	ADP
fcis-7987	78	13	the	the	DET
fcis-7987	78	14	data	datum	NOUN
fcis-7987	78	15	set	set	VERB
fcis-7987	78	16	dataset	dataset	VERB
fcis-7987	78	17	normal	normal	ADJ
fcis-7987	78	18	dos	do	NOUN
fcis-7987	78	19	probe	probe	VERB
fcis-7987	78	20	u2r	u2r	NOUN
fcis-7987	78	21	r2l	r2l	NOUN
fcis-7987	78	22	kddtrain	kddtrain	VERB
fcis-7987	78	23	13449	13449	NUM
fcis-7987	78	24	9834	9834	NUM
fcis-7987	78	25	2289	2289	NUM
fcis-7987	78	26	11	11	NUM
fcis-7987	78	27	209	209	NUM
fcis-7987	78	28	kddtrain+20	kddtrain+20	NOUN
fcis-7987	78	29	%	%	NOUN
fcis-7987	78	30	67343	67343	NUM
fcis-7987	78	31	45927	45927	NUM
fcis-7987	78	32	11656	11656	NUM
fcis-7987	79	1	52	52	NUM
fcis-7987	79	2	995	995	NUM
fcis-7987	79	3	kddtest++	kddtest++	ADJ
fcis-7987	79	4	9711	9711	NUM
fcis-7987	79	5	7460	7460	NUM
fcis-7987	79	6	2421	2421	NUM
fcis-7987	79	7	67	67	NUM
fcis-7987	79	8	2885	2885	NUM
fcis-7987	79	9	kddtest-21	kddtest-21	PROPN
fcis-7987	79	10	2152	2152	NUM
fcis-7987	79	11	4342	4342	NUM
fcis-7987	79	12	2402	2402	NUM
fcis-7987	79	13	200	200	NUM
fcis-7987	79	14	2754	2754	NUM
fcis-7987	79	15	3.3	3.3	NUM
fcis-7987	79	16	.	.	PUNCT
fcis-7987	80	1	performance	performance	NOUN
fcis-7987	80	2	metrics	metric	NOUN
fcis-7987	80	3	in	in	ADP
fcis-7987	80	4	the	the	DET
fcis-7987	80	5	field	field	NOUN
fcis-7987	80	6	of	of	ADP
fcis-7987	80	7	intrusion	intrusion	NOUN
fcis-7987	80	8	detection	detection	NOUN
fcis-7987	80	9	,	,	PUNCT
fcis-7987	80	10	normal	normal	ADJ
fcis-7987	80	11	traffic	traffic	NOUN
fcis-7987	80	12	samples	sample	NOUN
fcis-7987	80	13	are	be	AUX
fcis-7987	80	14	usually	usually	ADV
fcis-7987	80	15	referred	refer	VERB
fcis-7987	80	16	to	to	ADP
fcis-7987	80	17	as	as	ADP
fcis-7987	80	18	negative	negative	ADJ
fcis-7987	80	19	samples	sample	NOUN
fcis-7987	80	20	,	,	PUNCT
fcis-7987	80	21	while	while	SCONJ
fcis-7987	80	22	samples	sample	NOUN
fcis-7987	80	23	of	of	ADP
fcis-7987	80	24	attack	attack	NOUN
fcis-7987	80	25	types	type	NOUN
fcis-7987	80	26	are	be	AUX
fcis-7987	80	27	referred	refer	VERB
fcis-7987	80	28	to	to	ADP
fcis-7987	80	29	as	as	ADP
fcis-7987	80	30	positive	positive	ADJ
fcis-7987	80	31	samples	sample	NOUN
fcis-7987	80	32	.	.	PUNCT
fcis-7987	81	1	there	there	PRON
fcis-7987	81	2	are	be	VERB
fcis-7987	81	3	four	four	NUM
fcis-7987	81	4	possible	possible	ADJ
fcis-7987	81	5	outcomes	outcome	NOUN
fcis-7987	81	6	for	for	ADP
fcis-7987	81	7	all	all	DET
fcis-7987	81	8	samples	sample	NOUN
fcis-7987	81	9	that	that	PRON
fcis-7987	81	10	are	be	AUX
fcis-7987	81	11	detected	detect	VERB
fcis-7987	81	12	:	:	PUNCT
fcis-7987	81	13	(	(	PUNCT
fcis-7987	81	14	1	1	X
fcis-7987	81	15	)	)	PUNCT
fcis-7987	81	16	true	true	ADJ
fcis-7987	81	17	positive	positive	ADJ
fcis-7987	81	18	(	(	PUNCT
fcis-7987	81	19	tp	tp	NOUN
fcis-7987	81	20	):	):	PUNCT
fcis-7987	81	21	actual	actual	ADJ
fcis-7987	81	22	attack	attack	NOUN
fcis-7987	81	23	samples	sample	NOUN
fcis-7987	81	24	that	that	PRON
fcis-7987	81	25	are	be	AUX
fcis-7987	81	26	correctly	correctly	ADV
fcis-7987	81	27	identified	identify	VERB
fcis-7987	81	28	as	as	ADP
fcis-7987	81	29	attack	attack	NOUN
fcis-7987	81	30	samples	sample	NOUN
fcis-7987	81	31	.	.	PUNCT
fcis-7987	82	1	(	(	PUNCT
fcis-7987	82	2	2	2	X
fcis-7987	82	3	)	)	PUNCT
fcis-7987	82	4	false	false	ADJ
fcis-7987	82	5	positive	positive	ADJ
fcis-7987	82	6	(	(	PUNCT
fcis-7987	82	7	fp	fp	ADJ
fcis-7987	82	8	):	):	PUNCT
fcis-7987	82	9	actual	actual	ADJ
fcis-7987	82	10	normal	normal	ADJ
fcis-7987	82	11	samples	sample	NOUN
fcis-7987	82	12	that	that	PRON
fcis-7987	82	13	are	be	AUX
fcis-7987	82	14	incorrectly	incorrectly	ADV
fcis-7987	82	15	identified	identify	VERB
fcis-7987	82	16	as	as	ADP
fcis-7987	82	17	attack	attack	NOUN
fcis-7987	82	18	samples	sample	NOUN
fcis-7987	82	19	,	,	PUNCT
fcis-7987	82	20	also	also	ADV
fcis-7987	82	21	known	know	VERB
fcis-7987	82	22	as	as	ADP
fcis-7987	82	23	false	false	ADJ
fcis-7987	82	24	alarms	alarm	NOUN
fcis-7987	82	25	.	.	PUNCT
fcis-7987	83	1	(	(	PUNCT
fcis-7987	83	2	3	3	X
fcis-7987	83	3	)	)	PUNCT
fcis-7987	83	4	true	true	ADJ
fcis-7987	83	5	negative	negative	ADJ
fcis-7987	83	6	(	(	PUNCT
fcis-7987	83	7	tn	tn	NOUN
fcis-7987	83	8	):	):	PUNCT
fcis-7987	83	9	actual	actual	ADJ
fcis-7987	83	10	normal	normal	ADJ
fcis-7987	83	11	samples	sample	NOUN
fcis-7987	83	12	that	that	PRON
fcis-7987	83	13	are	be	AUX
fcis-7987	83	14	correctly	correctly	ADV
fcis-7987	83	15	identified	identify	VERB
fcis-7987	83	16	as	as	ADP
fcis-7987	83	17	normal	normal	ADJ
fcis-7987	83	18	samples	sample	NOUN
fcis-7987	83	19	.	.	PUNCT
fcis-7987	84	1	(	(	PUNCT
fcis-7987	84	2	4	4	X
fcis-7987	84	3	)	)	PUNCT
fcis-7987	84	4	false	false	ADJ
fcis-7987	84	5	negative	negative	ADJ
fcis-7987	84	6	(	(	PUNCT
fcis-7987	84	7	fn	fn	NOUN
fcis-7987	84	8	):	):	PUNCT
fcis-7987	84	9	actual	actual	ADJ
fcis-7987	84	10	attack	attack	NOUN
fcis-7987	84	11	samples	sample	NOUN
fcis-7987	84	12	that	that	PRON
fcis-7987	84	13	are	be	AUX
fcis-7987	84	14	incorrectly	incorrectly	ADV
fcis-7987	84	15	identified	identify	VERB
fcis-7987	84	16	as	as	ADP
fcis-7987	84	17	normal	normal	ADJ
fcis-7987	84	18	samples	sample	NOUN
fcis-7987	84	19	,	,	PUNCT
fcis-7987	84	20	also	also	ADV
fcis-7987	84	21	known	know	VERB
fcis-7987	84	22	as	as	ADP
fcis-7987	84	23	misses	miss	NOUN
fcis-7987	84	24	.	.	PUNCT
fcis-7987	85	1	there	there	PRON
fcis-7987	85	2	are	be	VERB
fcis-7987	85	3	four	four	NUM
fcis-7987	85	4	evaluation	evaluation	NOUN
fcis-7987	85	5	metrics	metric	NOUN
fcis-7987	85	6	for	for	ADP
fcis-7987	85	7	intrusion	intrusion	NOUN
fcis-7987	85	8	detection	detection	NOUN
fcis-7987	85	9	,	,	PUNCT
fcis-7987	85	10	as	as	SCONJ
fcis-7987	85	11	follows	follow	VERB
fcis-7987	85	12	:	:	PUNCT
fcis-7987	85	13	(	(	PUNCT
fcis-7987	85	14	1	1	X
fcis-7987	85	15	)	)	PUNCT
fcis-7987	85	16	accuracy	accuracy	NOUN
fcis-7987	85	17	(	(	PUNCT
fcis-7987	85	18	acc	acc	PROPN
fcis-7987	85	19	)	)	PUNCT
fcis-7987	85	20	represents	represent	VERB
fcis-7987	85	21	the	the	DET
fcis-7987	85	22	proportion	proportion	NOUN
fcis-7987	85	23	of	of	ADP
fcis-7987	85	24	successfully	successfully	ADV
fcis-7987	85	25	detected	detect	VERB
fcis-7987	85	26	samples	sample	NOUN
fcis-7987	85	27	to	to	ADP
fcis-7987	85	28	the	the	DET
fcis-7987	85	29	total	total	ADJ
fcis-7987	85	30	samples	sample	NOUN
fcis-7987	85	31	.	.	PUNCT
fcis-7987	86	1	a	a	DET
fcis-7987	86	2	higher	high	ADJ
fcis-7987	86	3	value	value	NOUN
fcis-7987	86	4	indicates	indicate	VERB
fcis-7987	86	5	a	a	DET
fcis-7987	86	6	better	well	ADJ
fcis-7987	86	7	model	model	NOUN
fcis-7987	86	8	.	.	PUNCT
fcis-7987	87	1	its	its	PRON
fcis-7987	87	2	calculation	calculation	NOUN
fcis-7987	87	3	formula	formula	NOUN
fcis-7987	87	4	is	be	AUX
fcis-7987	87	5	as	as	SCONJ
fcis-7987	87	6	follows	follow	VERB
fcis-7987	87	7	:	:	PUNCT
fcis-7987	87	8	accuracy(acc)=	accuracy(acc)=	VERB
fcis-7987	87	9	tp+tn	tp+tn	PRON
fcis-7987	87	10	tp+tn+fp+fn	tp+tn+fp+fn	PROPN
fcis-7987	87	11	(	(	PUNCT
fcis-7987	87	12	4	4	NUM
fcis-7987	87	13	)	)	PUNCT
fcis-7987	87	14	(	(	PUNCT
fcis-7987	87	15	2	2	X
fcis-7987	87	16	)	)	PUNCT
fcis-7987	87	17	precision	precision	NOUN
fcis-7987	87	18	represents	represent	VERB
fcis-7987	87	19	the	the	DET
fcis-7987	87	20	proportion	proportion	NOUN
fcis-7987	87	21	of	of	ADP
fcis-7987	87	22	actual	actual	ADJ
fcis-7987	87	23	attack	attack	NOUN
fcis-7987	87	24	samples	sample	NOUN
fcis-7987	87	25	among	among	ADP
fcis-7987	87	26	all	all	DET
fcis-7987	87	27	samples	sample	NOUN
fcis-7987	87	28	that	that	PRON
fcis-7987	87	29	are	be	AUX
fcis-7987	87	30	detected	detect	VERB
fcis-7987	87	31	as	as	ADP
fcis-7987	87	32	attack	attack	NOUN
fcis-7987	87	33	by	by	ADP
fcis-7987	87	34	the	the	DET
fcis-7987	87	35	model	model	NOUN
fcis-7987	87	36	.	.	PUNCT
fcis-7987	88	1	a	a	DET
fcis-7987	88	2	higher	high	ADJ
fcis-7987	88	3	value	value	NOUN
fcis-7987	88	4	indicates	indicate	VERB
fcis-7987	88	5	better	well	ADJ
fcis-7987	88	6	performance	performance	NOUN
fcis-7987	88	7	in	in	ADP
fcis-7987	88	8	detecting	detect	VERB
fcis-7987	88	9	attack	attack	NOUN
fcis-7987	88	10	behaviors	behavior	NOUN
fcis-7987	88	11	.	.	PUNCT
fcis-7987	89	1	its	its	PRON
fcis-7987	89	2	formula	formula	NOUN
fcis-7987	89	3	is	be	AUX
fcis-7987	89	4	:	:	PUNCT
fcis-7987	89	5	precision=	precision=	NUM
fcis-7987	89	6	tp	tp	ADP
fcis-7987	89	7	tp+fp	tp+fp	NUM
fcis-7987	89	8	(	(	PUNCT
fcis-7987	89	9	5	5	NUM
fcis-7987	89	10	)	)	PUNCT
fcis-7987	89	11	(	(	PUNCT
fcis-7987	89	12	3	3	X
fcis-7987	89	13	)	)	PUNCT
fcis-7987	89	14	recall	recall	NOUN
fcis-7987	89	15	represents	represent	VERB
fcis-7987	89	16	the	the	DET
fcis-7987	89	17	proportion	proportion	NOUN
fcis-7987	89	18	of	of	ADP
fcis-7987	89	19	samples	sample	NOUN
fcis-7987	89	20	identified	identify	VERB
fcis-7987	89	21	as	as	ADP
fcis-7987	89	22	attack	attack	NOUN
fcis-7987	89	23	type	type	NOUN
fcis-7987	89	24	among	among	ADP
fcis-7987	89	25	all	all	DET
fcis-7987	89	26	attack	attack	NOUN
fcis-7987	89	27	samples	sample	NOUN
fcis-7987	89	28	in	in	ADP
fcis-7987	89	29	the	the	DET
fcis-7987	89	30	dataset	dataset	NOUN
fcis-7987	89	31	.	.	PUNCT
fcis-7987	90	1	a	a	DET
fcis-7987	90	2	higher	high	ADJ
fcis-7987	90	3	value	value	NOUN
fcis-7987	90	4	indicates	indicate	VERB
fcis-7987	90	5	better	well	ADJ
fcis-7987	90	6	performance	performance	NOUN
fcis-7987	90	7	of	of	ADP
fcis-7987	90	8	the	the	DET
fcis-7987	90	9	model	model	NOUN
fcis-7987	90	10	.	.	PUNCT
fcis-7987	91	1	its	its	PRON
fcis-7987	91	2	formula	formula	NOUN
fcis-7987	91	3	is	be	AUX
fcis-7987	91	4	:	:	PUNCT
fcis-7987	91	5	recall=	recall=	NUM
fcis-7987	91	6	tp	tp	X
fcis-7987	91	7	tp+fn	tp+fn	X
fcis-7987	91	8	(	(	PUNCT
fcis-7987	91	9	6	6	NUM
fcis-7987	91	10	)	)	PUNCT
fcis-7987	91	11	(	(	PUNCT
fcis-7987	91	12	4	4	X
fcis-7987	91	13	)	)	PUNCT
fcis-7987	91	14	the	the	DET
fcis-7987	91	15	f1	f1	NOUN
fcis-7987	91	16	-	-	PUNCT
fcis-7987	91	17	score	score	NOUN
fcis-7987	91	18	is	be	AUX
fcis-7987	91	19	a	a	DET
fcis-7987	91	20	comprehensive	comprehensive	ADJ
fcis-7987	91	21	evaluation	evaluation	NOUN
fcis-7987	91	22	of	of	ADP
fcis-7987	91	23	precision	precision	NOUN
fcis-7987	91	24	and	and	CCONJ
fcis-7987	91	25	recall	recall	NOUN
fcis-7987	91	26	,	,	PUNCT
fcis-7987	91	27	with	with	ADP
fcis-7987	91	28	a	a	DET
fcis-7987	91	29	maximum	maximum	ADJ
fcis-7987	91	30	value	value	NOUN
fcis-7987	91	31	of	of	ADP
fcis-7987	91	32	1	1	NUM
fcis-7987	91	33	and	and	CCONJ
fcis-7987	91	34	a	a	DET
fcis-7987	91	35	minimum	minimum	ADJ
fcis-7987	91	36	value	value	NOUN
fcis-7987	91	37	of	of	ADP
fcis-7987	91	38	0	0	NUM
fcis-7987	91	39	.	.	PUNCT
fcis-7987	92	1	a	a	DET
fcis-7987	92	2	higher	high	ADJ
fcis-7987	92	3	value	value	NOUN
fcis-7987	92	4	indicates	indicate	VERB
fcis-7987	92	5	a	a	DET
fcis-7987	92	6	better	well	ADJ
fcis-7987	92	7	model	model	NOUN
fcis-7987	92	8	performance	performance	NOUN
fcis-7987	92	9	.	.	PUNCT
fcis-7987	93	1	its	its	PRON
fcis-7987	93	2	formula	formula	NOUN
fcis-7987	93	3	is	be	AUX
fcis-7987	93	4	:	:	PUNCT
fcis-7987	93	5	f1score=	f1score=	ADJ
fcis-7987	93	6	2×precision×recall	2×precision×recall	PROPN
fcis-7987	93	7	precision+recall	precision+recall	PROPN
fcis-7987	93	8	(	(	PUNCT
fcis-7987	93	9	7	7	NUM
fcis-7987	93	10	)	)	PUNCT
fcis-7987	93	11	3.4	3.4	NUM
fcis-7987	93	12	.	.	PUNCT
fcis-7987	94	1	parameter	parameter	NOUN
fcis-7987	94	2	settings	setting	NOUN
fcis-7987	94	3	the	the	DET
fcis-7987	94	4	parameter	parameter	NOUN
fcis-7987	94	5	settings	setting	NOUN
fcis-7987	94	6	for	for	ADP
fcis-7987	94	7	the	the	DET
fcis-7987	94	8	intrusion	intrusion	NOUN
fcis-7987	94	9	detection	detection	NOUN
fcis-7987	94	10	model	model	NOUN
fcis-7987	94	11	based	base	VERB
fcis-7987	94	12	on	on	ADP
fcis-7987	94	13	tbl	tbl	NOUN
fcis-7987	94	14	are	be	AUX
fcis-7987	94	15	shown	show	VERB
fcis-7987	94	16	in	in	ADP
fcis-7987	94	17	table	table	NOUN
fcis-7987	94	18	3	3	NUM
fcis-7987	94	19	:	:	PUNCT
fcis-7987	94	20	table	table	NOUN
fcis-7987	94	21	3	3	PROPN
fcis-7987	94	22	.	.	PUNCT
fcis-7987	94	23	model	model	NOUN
fcis-7987	94	24	parameters	parameter	NOUN
fcis-7987	94	25	parameters	parameter	NOUN
fcis-7987	94	26	value	value	VERB
fcis-7987	94	27	iterations	iteration	NOUN
fcis-7987	94	28	100	100	NUM
fcis-7987	94	29	batch	batch	NOUN
fcis-7987	94	30	size	size	NOUN
fcis-7987	94	31	2000	2000	NUM
fcis-7987	94	32	attention	attention	NOUN
fcis-7987	94	33	heads	head	NOUN
fcis-7987	94	34	4	4	NUM
fcis-7987	94	35	feedforward	feedforward	VERB
fcis-7987	94	36	hidden	hide	VERB
fcis-7987	94	37	nodes	node	NOUN
fcis-7987	94	38	64	64	NUM
fcis-7987	94	39	bilstm	bilstm	NOUN
fcis-7987	94	40	hidden	hide	VERB
fcis-7987	94	41	nodes	node	NOUN
fcis-7987	94	42	64	64	NUM
fcis-7987	94	43	learning	learning	NOUN
fcis-7987	94	44	rate	rate	NOUN
fcis-7987	94	45	0.0001	0.0001	NUM
fcis-7987	94	46	loss	loss	NOUN
fcis-7987	94	47	rate	rate	NOUN
fcis-7987	94	48	0.3	0.3	NUM
fcis-7987	94	49	3.5	3.5	NUM
fcis-7987	94	50	.	.	PUNCT
fcis-7987	95	1	analysis	analysis	NOUN
fcis-7987	95	2	of	of	ADP
fcis-7987	95	3	results	result	NOUN
fcis-7987	95	4	the	the	DET
fcis-7987	95	5	detection	detection	NOUN
fcis-7987	95	6	accuracy	accuracy	NOUN
fcis-7987	95	7	of	of	ADP
fcis-7987	95	8	the	the	DET
fcis-7987	95	9	intrusion	intrusion	NOUN
fcis-7987	95	10	detection	detection	NOUN
fcis-7987	95	11	models	model	NOUN
fcis-7987	95	12	based	base	VERB
fcis-7987	95	13	on	on	ADP
fcis-7987	95	14	the	the	DET
fcis-7987	95	15	commonly	commonly	ADV
fcis-7987	95	16	used	use	VERB
fcis-7987	95	17	machine	machine	NOUN
fcis-7987	95	18	learning	learn	VERB
fcis-7987	95	19	algorithms	algorithm	NOUN
fcis-7987	95	20	svm	svm	VERB
fcis-7987	95	21	and	and	CCONJ
fcis-7987	95	22	random	random	ADJ
fcis-7987	95	23	forest	forest	NOUN
fcis-7987	95	24	(	(	PUNCT
fcis-7987	95	25	rf	rf	NOUN
fcis-7987	95	26	)	)	PUNCT
fcis-7987	95	27	,	,	PUNCT
fcis-7987	95	28	as	as	ADV
fcis-7987	95	29	well	well	ADV
fcis-7987	95	30	as	as	ADP
fcis-7987	95	31	the	the	DET
fcis-7987	95	32	commonly	commonly	ADV
fcis-7987	95	33	used	use	VERB
fcis-7987	95	34	deep	deep	ADJ
fcis-7987	95	35	learning	learning	NOUN
fcis-7987	95	36	network	network	NOUN
fcis-7987	95	37	models	model	NOUN
fcis-7987	95	38	transformer	transformer	VERB
fcis-7987	95	39	and	and	CCONJ
fcis-7987	95	40	lstm	lstm	NOUN
fcis-7987	95	41	,	,	PUNCT
fcis-7987	95	42	were	be	AUX
fcis-7987	95	43	compared	compare	VERB
fcis-7987	95	44	in	in	ADP
fcis-7987	95	45	experimental	experimental	ADJ
fcis-7987	95	46	tests	test	NOUN
fcis-7987	95	47	,	,	PUNCT
fcis-7987	95	48	as	as	SCONJ
fcis-7987	95	49	shown	show	VERB
fcis-7987	95	50	in	in	ADP
fcis-7987	95	51	figure	figure	NOUN
fcis-7987	95	52	5	5	NUM
fcis-7987	95	53	.	.	PUNCT
fcis-7987	96	1	figure	figure	NOUN
fcis-7987	96	2	5	5	NUM
fcis-7987	96	3	.	.	PUNCT
fcis-7987	96	4	loss	loss	NOUN
fcis-7987	96	5	value	value	NOUN
fcis-7987	96	6	kddtest+	kddtest+	NOUN
fcis-7987	96	7	dataset	dataset	NOUN
fcis-7987	96	8	,	,	PUNCT
fcis-7987	96	9	and	and	CCONJ
fcis-7987	96	10	it	it	PRON
fcis-7987	96	11	can	can	AUX
fcis-7987	96	12	be	be	AUX
fcis-7987	96	13	seen	see	VERB
fcis-7987	96	14	from	from	ADP
fcis-7987	96	15	the	the	DET
fcis-7987	96	16	comparison	comparison	NOUN
fcis-7987	96	17	in	in	ADP
fcis-7987	96	18	the	the	DET
fcis-7987	96	19	figure	figure	NOUN
fcis-7987	96	20	that	that	SCONJ
fcis-7987	96	21	the	the	DET
fcis-7987	96	22	tbl	tbl	NOUN
fcis-7987	96	23	method	method	NOUN
fcis-7987	96	24	designed	design	VERB
fcis-7987	96	25	in	in	ADP
fcis-7987	96	26	this	this	DET
fcis-7987	96	27	chapter	chapter	NOUN
fcis-7987	96	28	has	have	VERB
fcis-7987	96	29	a	a	DET
fcis-7987	96	30	significant	significant	ADJ
fcis-7987	96	31	advantage	advantage	NOUN
fcis-7987	96	32	over	over	ADP
fcis-7987	96	33	other	other	ADJ
fcis-7987	96	34	methods	method	NOUN
fcis-7987	96	35	,	,	PUNCT
fcis-7987	96	36	with	with	ADP
fcis-7987	96	37	an	an	DET
fcis-7987	96	38	accuracy	accuracy	NOUN
fcis-7987	96	39	of	of	ADP
fcis-7987	96	40	94.3	94.3	NUM
fcis-7987	96	41	%	%	NOUN
fcis-7987	96	42	.	.	PUNCT
fcis-7987	97	1	therefore	therefore	ADV
fcis-7987	97	2	,	,	PUNCT
fcis-7987	97	3	the	the	DET
fcis-7987	97	4	methods	method	NOUN
fcis-7987	97	5	discussed	discuss	VERB
fcis-7987	97	6	in	in	ADP
fcis-7987	97	7	this	this	DET
fcis-7987	97	8	chapter	chapter	NOUN
fcis-7987	97	9	are	be	AUX
fcis-7987	97	10	feasible	feasible	ADJ
fcis-7987	97	11	.	.	PUNCT
fcis-7987	98	1	25	25	NUM
fcis-7987	98	2	figure	figure	NOUN
fcis-7987	98	3	6	6	NUM
fcis-7987	98	4	.	.	PUNCT
fcis-7987	98	5	accuracy	accuracy	NOUN
fcis-7987	98	6	figure	figure	NOUN
fcis-7987	98	7	6	6	NUM
fcis-7987	98	8	compares	compare	VERB
fcis-7987	98	9	the	the	DET
fcis-7987	98	10	five	five	NUM
fcis-7987	98	11	-	-	PUNCT
fcis-7987	98	12	class	class	NOUN
fcis-7987	98	13	precision	precision	NOUN
fcis-7987	98	14	of	of	ADP
fcis-7987	98	15	different	different	ADJ
fcis-7987	98	16	models	model	NOUN
fcis-7987	98	17	on	on	ADP
fcis-7987	98	18	the	the	DET
fcis-7987	98	19	kddtest+	kddtest+	NOUN
fcis-7987	98	20	dataset	dataset	NOUN
fcis-7987	98	21	.	.	PUNCT
fcis-7987	99	1	the	the	DET
fcis-7987	99	2	data	datum	NOUN
fcis-7987	99	3	from	from	ADP
fcis-7987	99	4	the	the	DET
fcis-7987	99	5	graph	graph	NOUN
fcis-7987	99	6	shows	show	VERB
fcis-7987	99	7	that	that	SCONJ
fcis-7987	99	8	the	the	DET
fcis-7987	99	9	tbl	tbl	NOUN
fcis-7987	99	10	-	-	PUNCT
fcis-7987	99	11	based	base	VERB
fcis-7987	99	12	intrusion	intrusion	NOUN
fcis-7987	99	13	detection	detection	NOUN
fcis-7987	99	14	method	method	NOUN
fcis-7987	99	15	designed	design	VERB
fcis-7987	99	16	in	in	ADP
fcis-7987	99	17	this	this	DET
fcis-7987	99	18	chapter	chapter	NOUN
fcis-7987	99	19	has	have	VERB
fcis-7987	99	20	certain	certain	ADJ
fcis-7987	99	21	advantages	advantage	NOUN
fcis-7987	99	22	in	in	ADP
fcis-7987	99	23	detecting	detect	VERB
fcis-7987	99	24	the	the	DET
fcis-7987	99	25	attack	attack	NOUN
fcis-7987	99	26	types	type	NOUN
fcis-7987	99	27	probe	probe	NOUN
fcis-7987	99	28	,	,	PUNCT
fcis-7987	99	29	r2l	r2l	NOUN
fcis-7987	99	30	,	,	PUNCT
fcis-7987	99	31	and	and	CCONJ
fcis-7987	99	32	u2r	u2r	NOUN
fcis-7987	99	33	,	,	PUNCT
fcis-7987	99	34	with	with	ADP
fcis-7987	99	35	precision	precision	NOUN
fcis-7987	99	36	rates	rate	NOUN
fcis-7987	99	37	of	of	ADP
fcis-7987	99	38	94.2	94.2	NUM
fcis-7987	99	39	%	%	NOUN
fcis-7987	99	40	,	,	PUNCT
fcis-7987	99	41	92.6	92.6	NUM
fcis-7987	99	42	%	%	NOUN
fcis-7987	99	43	,	,	PUNCT
fcis-7987	99	44	and	and	CCONJ
fcis-7987	99	45	98.8	98.8	NUM
fcis-7987	99	46	%	%	NOUN
fcis-7987	99	47	,	,	PUNCT
fcis-7987	99	48	respectively	respectively	ADV
fcis-7987	99	49	.	.	PUNCT
fcis-7987	100	1	however	however	ADV
fcis-7987	100	2	,	,	PUNCT
fcis-7987	100	3	the	the	DET
fcis-7987	100	4	performance	performance	NOUN
fcis-7987	100	5	of	of	ADP
fcis-7987	100	6	this	this	DET
fcis-7987	100	7	method	method	NOUN
fcis-7987	100	8	in	in	ADP
fcis-7987	100	9	detecting	detect	VERB
fcis-7987	100	10	normal	normal	ADJ
fcis-7987	100	11	and	and	CCONJ
fcis-7987	100	12	dos	do	NOUN
fcis-7987	100	13	types	type	NOUN
fcis-7987	100	14	of	of	ADP
fcis-7987	100	15	data	datum	NOUN
fcis-7987	100	16	is	be	AUX
fcis-7987	100	17	not	not	PART
fcis-7987	100	18	as	as	ADV
fcis-7987	100	19	good	good	ADJ
fcis-7987	100	20	as	as	ADP
fcis-7987	100	21	that	that	PRON
fcis-7987	100	22	of	of	ADP
fcis-7987	100	23	the	the	DET
fcis-7987	100	24	transformer	transformer	NOUN
fcis-7987	100	25	-	-	PUNCT
fcis-7987	100	26	based	base	VERB
fcis-7987	100	27	intrusion	intrusion	NOUN
fcis-7987	100	28	detection	detection	NOUN
fcis-7987	100	29	method	method	NOUN
fcis-7987	100	30	.	.	PUNCT
fcis-7987	101	1	the	the	DET
fcis-7987	101	2	method	method	NOUN
fcis-7987	101	3	designed	design	VERB
fcis-7987	101	4	in	in	ADP
fcis-7987	101	5	this	this	DET
fcis-7987	101	6	chapter	chapter	NOUN
fcis-7987	101	7	has	have	VERB
fcis-7987	101	8	a	a	DET
fcis-7987	101	9	detection	detection	NOUN
fcis-7987	101	10	effect	effect	NOUN
fcis-7987	101	11	on	on	ADP
fcis-7987	101	12	normal	normal	ADJ
fcis-7987	101	13	data	datum	NOUN
fcis-7987	101	14	that	that	PRON
fcis-7987	101	15	is	be	AUX
fcis-7987	101	16	lower	low	ADJ
fcis-7987	101	17	than	than	ADP
fcis-7987	101	18	the	the	DET
fcis-7987	101	19	best	good	ADJ
fcis-7987	101	20	by	by	ADP
fcis-7987	101	21	1.6	1.6	NUM
fcis-7987	101	22	%	%	NOUN
fcis-7987	101	23	and	and	CCONJ
fcis-7987	101	24	a	a	DET
fcis-7987	101	25	detection	detection	NOUN
fcis-7987	101	26	effect	effect	NOUN
fcis-7987	101	27	on	on	ADP
fcis-7987	101	28	dos	do	NOUN
fcis-7987	101	29	type	type	NOUN
fcis-7987	101	30	data	datum	NOUN
fcis-7987	101	31	that	that	PRON
fcis-7987	101	32	is	be	AUX
fcis-7987	101	33	lower	low	ADJ
fcis-7987	101	34	than	than	ADP
fcis-7987	101	35	the	the	DET
fcis-7987	101	36	best	good	ADJ
fcis-7987	101	37	by	by	ADP
fcis-7987	101	38	2.0	2.0	NUM
fcis-7987	101	39	%	%	NOUN
fcis-7987	101	40	.	.	PUNCT
fcis-7987	102	1	overall	overall	ADV
fcis-7987	102	2	,	,	PUNCT
fcis-7987	102	3	the	the	DET
fcis-7987	102	4	method	method	NOUN
fcis-7987	102	5	designed	design	VERB
fcis-7987	102	6	in	in	ADP
fcis-7987	102	7	this	this	DET
fcis-7987	102	8	chapter	chapter	NOUN
fcis-7987	102	9	is	be	AUX
fcis-7987	102	10	still	still	ADV
fcis-7987	102	11	effective	effective	ADJ
fcis-7987	102	12	.	.	PUNCT
fcis-7987	103	1	figure	figure	NOUN
fcis-7987	103	2	7	7	NUM
fcis-7987	103	3	.	.	PUNCT
fcis-7987	103	4	recall	recall	PROPN
fcis-7987	103	5	figure	figure	NOUN
fcis-7987	103	6	7	7	NUM
fcis-7987	103	7	shows	show	VERB
fcis-7987	103	8	the	the	DET
fcis-7987	103	9	recall	recall	NOUN
fcis-7987	103	10	rate	rate	NOUN
fcis-7987	103	11	of	of	ADP
fcis-7987	103	12	different	different	ADJ
fcis-7987	103	13	methods	method	NOUN
fcis-7987	103	14	in	in	ADP
fcis-7987	103	15	detecting	detect	VERB
fcis-7987	103	16	the	the	DET
fcis-7987	103	17	kddtest+	kddtest+	NOUN
fcis-7987	103	18	dataset	dataset	NOUN
fcis-7987	103	19	,	,	PUNCT
fcis-7987	103	20	which	which	PRON
fcis-7987	103	21	mainly	mainly	ADV
fcis-7987	103	22	expresses	express	VERB
fcis-7987	103	23	the	the	DET
fcis-7987	103	24	probability	probability	NOUN
fcis-7987	103	25	that	that	SCONJ
fcis-7987	103	26	each	each	DET
fcis-7987	103	27	type	type	NOUN
fcis-7987	103	28	of	of	ADP
fcis-7987	103	29	data	datum	NOUN
fcis-7987	103	30	can	can	AUX
fcis-7987	103	31	be	be	AUX
fcis-7987	103	32	correctly	correctly	ADV
fcis-7987	103	33	distinguished	distinguish	VERB
fcis-7987	103	34	in	in	ADP
fcis-7987	103	35	different	different	ADJ
fcis-7987	103	36	types	type	NOUN
fcis-7987	103	37	of	of	ADP
fcis-7987	103	38	data	datum	NOUN
fcis-7987	103	39	.	.	PUNCT
fcis-7987	104	1	from	from	ADP
fcis-7987	104	2	the	the	DET
fcis-7987	104	3	figure	figure	NOUN
fcis-7987	104	4	,	,	PUNCT
fcis-7987	104	5	it	it	PRON
fcis-7987	104	6	can	can	AUX
fcis-7987	104	7	be	be	AUX
fcis-7987	104	8	seen	see	VERB
fcis-7987	104	9	that	that	SCONJ
fcis-7987	104	10	for	for	ADP
fcis-7987	104	11	normal	normal	ADJ
fcis-7987	104	12	type	type	NOUN
fcis-7987	104	13	data	datum	NOUN
fcis-7987	104	14	,	,	PUNCT
fcis-7987	104	15	the	the	DET
fcis-7987	104	16	recall	recall	NOUN
fcis-7987	104	17	rate	rate	NOUN
fcis-7987	104	18	of	of	ADP
fcis-7987	104	19	the	the	DET
fcis-7987	104	20	rf	rf	NOUN
fcis-7987	104	21	method	method	NOUN
fcis-7987	104	22	is	be	AUX
fcis-7987	104	23	the	the	DET
fcis-7987	104	24	highest	high	ADJ
fcis-7987	104	25	at	at	ADP
fcis-7987	104	26	95.8	95.8	NUM
fcis-7987	104	27	%	%	NOUN
fcis-7987	104	28	,	,	PUNCT
fcis-7987	104	29	but	but	CCONJ
fcis-7987	104	30	the	the	DET
fcis-7987	104	31	rf	rf	NOUN
fcis-7987	104	32	model	model	NOUN
fcis-7987	104	33	's	's	PART
fcis-7987	104	34	recall	recall	NOUN
fcis-7987	104	35	rate	rate	NOUN
fcis-7987	104	36	for	for	ADP
fcis-7987	104	37	probe	probe	NOUN
fcis-7987	104	38	,	,	PUNCT
fcis-7987	104	39	r2l	r2l	NOUN
fcis-7987	104	40	,	,	PUNCT
fcis-7987	104	41	and	and	CCONJ
fcis-7987	104	42	u2r	u2r	NOUN
fcis-7987	104	43	type	type	NOUN
fcis-7987	104	44	data	datum	NOUN
fcis-7987	104	45	is	be	AUX
fcis-7987	104	46	not	not	PART
fcis-7987	104	47	good	good	ADJ
fcis-7987	104	48	.	.	PUNCT
fcis-7987	105	1	the	the	DET
fcis-7987	105	2	tblbased	tblbase	VERB
fcis-7987	105	3	intrusion	intrusion	NOUN
fcis-7987	105	4	detection	detection	NOUN
fcis-7987	105	5	method	method	NOUN
fcis-7987	105	6	designed	design	VERB
fcis-7987	105	7	in	in	ADP
fcis-7987	105	8	this	this	DET
fcis-7987	105	9	chapter	chapter	NOUN
fcis-7987	105	10	has	have	AUX
fcis-7987	105	11	improved	improve	VERB
fcis-7987	105	12	the	the	DET
fcis-7987	105	13	recall	recall	NOUN
fcis-7987	105	14	rate	rate	NOUN
fcis-7987	105	15	on	on	ADP
fcis-7987	105	16	normal	normal	ADJ
fcis-7987	105	17	and	and	CCONJ
fcis-7987	105	18	dos	do	NOUN
fcis-7987	105	19	type	type	NOUN
fcis-7987	105	20	data	datum	NOUN
fcis-7987	105	21	by	by	ADP
fcis-7987	105	22	2.1	2.1	NUM
fcis-7987	105	23	%	%	NOUN
fcis-7987	105	24	and	and	CCONJ
fcis-7987	105	25	5.8	5.8	NUM
fcis-7987	105	26	%	%	NOUN
fcis-7987	105	27	,	,	PUNCT
fcis-7987	105	28	respectively	respectively	ADV
fcis-7987	105	29	,	,	PUNCT
fcis-7987	105	30	compared	compare	VERB
fcis-7987	105	31	to	to	ADP
fcis-7987	105	32	the	the	DET
fcis-7987	105	33	transformer	transformer	NOUN
fcis-7987	105	34	-	-	PUNCT
fcis-7987	105	35	based	base	VERB
fcis-7987	105	36	intrusion	intrusion	NOUN
fcis-7987	105	37	detection	detection	NOUN
fcis-7987	105	38	method	method	NOUN
fcis-7987	105	39	.	.	PUNCT
fcis-7987	106	1	however	however	ADV
fcis-7987	106	2	,	,	PUNCT
fcis-7987	106	3	the	the	DET
fcis-7987	106	4	recall	recall	NOUN
fcis-7987	106	5	rate	rate	NOUN
fcis-7987	106	6	of	of	ADP
fcis-7987	106	7	the	the	DET
fcis-7987	106	8	tbl	tbl	NOUN
fcis-7987	106	9	model	model	NOUN
fcis-7987	106	10	in	in	ADP
fcis-7987	106	11	detecting	detect	VERB
fcis-7987	106	12	probe	probe	NOUN
fcis-7987	106	13	,	,	PUNCT
fcis-7987	106	14	r2l	r2l	NOUN
fcis-7987	106	15	,	,	PUNCT
fcis-7987	106	16	and	and	CCONJ
fcis-7987	106	17	u2r	u2r	NOUN
fcis-7987	106	18	type	type	NOUN
fcis-7987	106	19	data	datum	NOUN
fcis-7987	106	20	is	be	AUX
fcis-7987	106	21	slightly	slightly	ADV
fcis-7987	106	22	lower	low	ADJ
fcis-7987	106	23	than	than	ADP
fcis-7987	106	24	that	that	PRON
fcis-7987	106	25	of	of	ADP
fcis-7987	106	26	the	the	DET
fcis-7987	106	27	transformer	transformer	NOUN
fcis-7987	106	28	model	model	NOUN
fcis-7987	106	29	.	.	PUNCT
fcis-7987	107	1	the	the	DET
fcis-7987	107	2	tbl	tbl	NOUN
fcis-7987	107	3	model	model	NOUN
fcis-7987	107	4	proposed	propose	VERB
fcis-7987	107	5	in	in	ADP
fcis-7987	107	6	this	this	DET
fcis-7987	107	7	chapter	chapter	NOUN
fcis-7987	107	8	is	be	AUX
fcis-7987	107	9	an	an	DET
fcis-7987	107	10	attempt	attempt	NOUN
fcis-7987	107	11	in	in	ADP
fcis-7987	107	12	the	the	DET
fcis-7987	107	13	field	field	NOUN
fcis-7987	107	14	of	of	ADP
fcis-7987	107	15	intrusion	intrusion	NOUN
fcis-7987	107	16	detection	detection	NOUN
fcis-7987	107	17	.	.	PUNCT
fcis-7987	108	1	comparing	compare	VERB
fcis-7987	108	2	the	the	DET
fcis-7987	108	3	precision	precision	NOUN
fcis-7987	108	4	and	and	CCONJ
fcis-7987	108	5	recall	recall	NOUN
fcis-7987	108	6	rates	rate	NOUN
fcis-7987	108	7	can	can	AUX
fcis-7987	108	8	show	show	VERB
fcis-7987	108	9	that	that	SCONJ
fcis-7987	108	10	this	this	DET
fcis-7987	108	11	model	model	NOUN
fcis-7987	108	12	has	have	VERB
fcis-7987	108	13	certain	certain	ADJ
fcis-7987	108	14	effects	effect	NOUN
fcis-7987	108	15	and	and	CCONJ
fcis-7987	108	16	provides	provide	VERB
fcis-7987	108	17	a	a	DET
fcis-7987	108	18	new	new	ADJ
fcis-7987	108	19	direction	direction	NOUN
fcis-7987	108	20	for	for	ADP
fcis-7987	108	21	intrusion	intrusion	NOUN
fcis-7987	108	22	detection	detection	NOUN
fcis-7987	108	23	based	base	VERB
fcis-7987	108	24	on	on	ADP
fcis-7987	108	25	transformer	transformer	NOUN
fcis-7987	108	26	.	.	PUNCT
fcis-7987	109	1	to	to	PART
fcis-7987	109	2	provide	provide	VERB
fcis-7987	109	3	a	a	DET
fcis-7987	109	4	more	more	ADV
fcis-7987	109	5	intuitive	intuitive	ADJ
fcis-7987	109	6	comparison	comparison	NOUN
fcis-7987	109	7	of	of	ADP
fcis-7987	109	8	the	the	DET
fcis-7987	109	9	performance	performance	NOUN
fcis-7987	109	10	of	of	ADP
fcis-7987	109	11	different	different	ADJ
fcis-7987	109	12	models	model	NOUN
fcis-7987	109	13	,	,	PUNCT
fcis-7987	109	14	the	the	DET
fcis-7987	109	15	f1	f1	NOUN
fcis-7987	109	16	values	value	NOUN
fcis-7987	109	17	were	be	AUX
fcis-7987	109	18	compared	compare	VERB
fcis-7987	109	19	to	to	PART
fcis-7987	109	20	evaluate	evaluate	VERB
fcis-7987	109	21	the	the	DET
fcis-7987	109	22	overall	overall	ADJ
fcis-7987	109	23	performance	performance	NOUN
fcis-7987	109	24	of	of	ADP
fcis-7987	109	25	the	the	DET
fcis-7987	109	26	models	model	NOUN
fcis-7987	109	27	.	.	PUNCT
fcis-7987	110	1	the	the	DET
fcis-7987	110	2	f1	f1	PROPN
fcis-7987	110	3	values	value	NOUN
fcis-7987	110	4	of	of	ADP
fcis-7987	110	5	each	each	DET
fcis-7987	110	6	model	model	NOUN
fcis-7987	110	7	are	be	AUX
fcis-7987	110	8	shown	show	VERB
fcis-7987	110	9	in	in	ADP
fcis-7987	110	10	the	the	DET
fcis-7987	110	11	following	follow	VERB
fcis-7987	110	12	figure	figure	NOUN
fcis-7987	110	13	8	8	NUM
fcis-7987	110	14	.	.	PUNCT
fcis-7987	111	1	figure	figure	NOUN
fcis-7987	111	2	8	8	NUM
fcis-7987	111	3	.	.	PUNCT
fcis-7987	112	1	f1	f1	NOUN
fcis-7987	112	2	the	the	DET
fcis-7987	112	3	f1	f1	NOUN
fcis-7987	112	4	score	score	NOUN
fcis-7987	112	5	is	be	AUX
fcis-7987	112	6	generally	generally	ADV
fcis-7987	112	7	an	an	DET
fcis-7987	112	8	important	important	ADJ
fcis-7987	112	9	indicator	indicator	NOUN
fcis-7987	112	10	for	for	ADP
fcis-7987	112	11	overall	overall	ADJ
fcis-7987	112	12	performance	performance	NOUN
fcis-7987	112	13	comparison	comparison	NOUN
fcis-7987	112	14	of	of	ADP
fcis-7987	112	15	models	model	NOUN
fcis-7987	112	16	.	.	PUNCT
fcis-7987	113	1	the	the	DET
fcis-7987	113	2	f1	f1	PROPN
fcis-7987	113	3	score	score	NOUN
fcis-7987	113	4	comparison	comparison	NOUN
fcis-7987	113	5	of	of	ADP
fcis-7987	113	6	different	different	ADJ
fcis-7987	113	7	models	model	NOUN
fcis-7987	113	8	for	for	ADP
fcis-7987	113	9	each	each	DET
fcis-7987	113	10	data	datum	NOUN
fcis-7987	113	11	type	type	NOUN
fcis-7987	113	12	is	be	AUX
fcis-7987	113	13	shown	show	VERB
fcis-7987	113	14	in	in	ADP
fcis-7987	113	15	figure	figure	NOUN
fcis-7987	113	16	8	8	NUM
fcis-7987	113	17	.	.	PUNCT
fcis-7987	113	18	from	from	ADP
fcis-7987	113	19	the	the	DET
fcis-7987	113	20	figure	figure	NOUN
fcis-7987	113	21	,	,	PUNCT
fcis-7987	113	22	it	it	PRON
fcis-7987	113	23	can	can	AUX
fcis-7987	113	24	be	be	AUX
fcis-7987	113	25	seen	see	VERB
fcis-7987	113	26	that	that	SCONJ
fcis-7987	113	27	the	the	DET
fcis-7987	113	28	tbl	tbl	NOUN
fcis-7987	113	29	model	model	NOUN
fcis-7987	113	30	proposed	propose	VERB
fcis-7987	113	31	in	in	ADP
fcis-7987	113	32	this	this	DET
fcis-7987	113	33	paper	paper	NOUN
fcis-7987	113	34	has	have	VERB
fcis-7987	113	35	an	an	DET
fcis-7987	113	36	overall	overall	ADJ
fcis-7987	113	37	advantage	advantage	NOUN
fcis-7987	113	38	and	and	CCONJ
fcis-7987	113	39	achieved	achieve	VERB
fcis-7987	113	40	91.1	91.1	NUM
fcis-7987	113	41	%	%	NOUN
fcis-7987	113	42	,	,	PUNCT
fcis-7987	113	43	90.4	90.4	NUM
fcis-7987	113	44	%	%	NOUN
fcis-7987	113	45	,	,	PUNCT
fcis-7987	113	46	95.87	95.87	NUM
fcis-7987	113	47	%	%	NOUN
fcis-7987	113	48	,	,	PUNCT
fcis-7987	113	49	94.94	94.94	NUM
fcis-7987	113	50	%	%	NOUN
fcis-7987	113	51	,	,	PUNCT
fcis-7987	113	52	and	and	CCONJ
fcis-7987	113	53	99.2	99.2	NUM
fcis-7987	113	54	%	%	NOUN
fcis-7987	113	55	for	for	ADP
fcis-7987	113	56	the	the	DET
fcis-7987	113	57	five	five	NUM
fcis-7987	113	58	different	different	ADJ
fcis-7987	113	59	types	type	NOUN
fcis-7987	113	60	of	of	ADP
fcis-7987	113	61	data	datum	NOUN
fcis-7987	113	62	,	,	PUNCT
fcis-7987	113	63	respectively	respectively	ADV
fcis-7987	113	64	.	.	PUNCT
fcis-7987	114	1	this	this	PRON
fcis-7987	114	2	indicates	indicate	VERB
fcis-7987	114	3	that	that	SCONJ
fcis-7987	114	4	the	the	DET
fcis-7987	114	5	tbl	tbl	NOUN
fcis-7987	114	6	-	-	PUNCT
fcis-7987	114	7	based	base	VERB
fcis-7987	114	8	intrusion	intrusion	NOUN
fcis-7987	114	9	detection	detection	NOUN
fcis-7987	114	10	method	method	NOUN
fcis-7987	114	11	proposed	propose	VERB
fcis-7987	114	12	in	in	ADP
fcis-7987	114	13	this	this	DET
fcis-7987	114	14	chapter	chapter	NOUN
fcis-7987	114	15	is	be	AUX
fcis-7987	114	16	worth	worth	ADJ
fcis-7987	114	17	studying	study	VERB
fcis-7987	114	18	.	.	PUNCT
fcis-7987	115	1	4	4	X
fcis-7987	115	2	.	.	X
fcis-7987	115	3	conclusion	conclusion	NOUN
fcis-7987	115	4	with	with	ADP
fcis-7987	115	5	the	the	DET
fcis-7987	115	6	advancement	advancement	NOUN
fcis-7987	115	7	of	of	ADP
fcis-7987	115	8	technology	technology	NOUN
fcis-7987	115	9	,	,	PUNCT
fcis-7987	115	10	the	the	DET
fcis-7987	115	11	development	development	NOUN
fcis-7987	115	12	of	of	ADP
fcis-7987	115	13	various	various	ADJ
fcis-7987	115	14	industries	industry	NOUN
fcis-7987	115	15	has	have	AUX
fcis-7987	115	16	become	become	VERB
fcis-7987	115	17	inseparable	inseparable	ADJ
fcis-7987	115	18	from	from	ADP
fcis-7987	115	19	information	information	NOUN
fcis-7987	115	20	technology	technology	NOUN
fcis-7987	115	21	.	.	PUNCT
fcis-7987	116	1	people	people	NOUN
fcis-7987	116	2	's	's	PART
fcis-7987	116	3	lives	life	NOUN
fcis-7987	116	4	have	have	AUX
fcis-7987	116	5	become	become	VERB
fcis-7987	116	6	closely	closely	ADV
fcis-7987	116	7	related	relate	VERB
fcis-7987	116	8	to	to	ADP
fcis-7987	116	9	the	the	DET
fcis-7987	116	10	internet	internet	NOUN
fcis-7987	116	11	,	,	PUNCT
fcis-7987	116	12	and	and	CCONJ
fcis-7987	116	13	while	while	SCONJ
fcis-7987	116	14	the	the	DET
fcis-7987	116	15	network	network	NOUN
fcis-7987	116	16	facilitates	facilitate	VERB
fcis-7987	116	17	our	our	PRON
fcis-7987	116	18	lives	life	NOUN
fcis-7987	116	19	,	,	PUNCT
fcis-7987	116	20	it	it	PRON
fcis-7987	116	21	also	also	ADV
fcis-7987	116	22	generates	generate	VERB
fcis-7987	116	23	massive	massive	ADJ
fcis-7987	116	24	amounts	amount	NOUN
fcis-7987	116	25	of	of	ADP
fcis-7987	116	26	data	datum	NOUN
fcis-7987	116	27	,	,	PUNCT
fcis-7987	116	28	including	include	VERB
fcis-7987	116	29	sensitive	sensitive	ADJ
fcis-7987	116	30	data	datum	NOUN
fcis-7987	116	31	involving	involve	VERB
fcis-7987	116	32	personal	personal	ADJ
fcis-7987	116	33	privacy	privacy	NOUN
fcis-7987	116	34	.	.	PUNCT
fcis-7987	117	1	once	once	SCONJ
fcis-7987	117	2	such	such	ADJ
fcis-7987	117	3	data	datum	NOUN
fcis-7987	117	4	is	be	AUX
fcis-7987	117	5	leaked	leak	VERB
fcis-7987	117	6	,	,	PUNCT
fcis-7987	117	7	the	the	DET
fcis-7987	117	8	resulting	result	VERB
fcis-7987	117	9	losses	loss	NOUN
fcis-7987	117	10	are	be	AUX
fcis-7987	117	11	incalculable	incalculable	ADJ
fcis-7987	117	12	.	.	PUNCT
fcis-7987	118	1	therefore	therefore	ADV
fcis-7987	118	2	,	,	PUNCT
fcis-7987	118	3	network	network	NOUN
fcis-7987	118	4	security	security	NOUN
fcis-7987	118	5	issues	issue	NOUN
fcis-7987	118	6	have	have	AUX
fcis-7987	118	7	received	receive	VERB
fcis-7987	118	8	increasing	increase	VERB
fcis-7987	118	9	attention	attention	NOUN
fcis-7987	118	10	.	.	PUNCT
fcis-7987	119	1	with	with	ADP
fcis-7987	119	2	the	the	DET
fcis-7987	119	3	complexity	complexity	NOUN
fcis-7987	119	4	and	and	CCONJ
fcis-7987	119	5	diversity	diversity	NOUN
fcis-7987	119	6	of	of	ADP
fcis-7987	119	7	attack	attack	NOUN
fcis-7987	119	8	methods	method	NOUN
fcis-7987	119	9	,	,	PUNCT
fcis-7987	119	10	traditional	traditional	ADJ
fcis-7987	119	11	firewall	firewall	NOUN
fcis-7987	119	12	technologies	technology	NOUN
fcis-7987	119	13	can	can	AUX
fcis-7987	119	14	no	no	ADV
fcis-7987	119	15	longer	long	ADV
fcis-7987	119	16	meet	meet	VERB
fcis-7987	119	17	the	the	DET
fcis-7987	119	18	current	current	ADJ
fcis-7987	119	19	security	security	NOUN
fcis-7987	119	20	needs	need	NOUN
fcis-7987	119	21	.	.	PUNCT
fcis-7987	120	1	in	in	ADP
fcis-7987	120	2	contrast	contrast	NOUN
fcis-7987	120	3	,	,	PUNCT
fcis-7987	120	4	intrusion	intrusion	NOUN
fcis-7987	120	5	detection	detection	NOUN
fcis-7987	120	6	methods	method	NOUN
fcis-7987	120	7	based	base	VERB
fcis-7987	120	8	on	on	ADP
fcis-7987	120	9	deep	deep	ADJ
fcis-7987	120	10	learning	learning	NOUN
fcis-7987	120	11	can	can	AUX
fcis-7987	120	12	automatically	automatically	ADV
fcis-7987	120	13	discover	discover	VERB
fcis-7987	120	14	hidden	hidden	ADJ
fcis-7987	120	15	associations	association	NOUN
fcis-7987	120	16	in	in	ADP
fcis-7987	120	17	traffic	traffic	NOUN
fcis-7987	120	18	data	datum	NOUN
fcis-7987	120	19	,	,	PUNCT
fcis-7987	120	20	solve	solve	VERB
fcis-7987	120	21	the	the	DET
fcis-7987	120	22	problem	problem	NOUN
fcis-7987	120	23	of	of	ADP
fcis-7987	120	24	manual	manual	ADJ
fcis-7987	120	25	feature	feature	NOUN
fcis-7987	120	26	selection	selection	NOUN
fcis-7987	120	27	in	in	ADP
fcis-7987	120	28	traditional	traditional	ADJ
fcis-7987	120	29	methods	method	NOUN
fcis-7987	120	30	,	,	PUNCT
fcis-7987	120	31	and	and	CCONJ
fcis-7987	120	32	can	can	AUX
fcis-7987	120	33	better	well	ADV
fcis-7987	120	34	identify	identify	VERB
fcis-7987	120	35	unknown	unknown	ADJ
fcis-7987	120	36	attack	attack	NOUN
fcis-7987	120	37	types	type	NOUN
fcis-7987	120	38	and	and	CCONJ
fcis-7987	120	39	adapt	adapt	VERB
fcis-7987	120	40	to	to	ADP
fcis-7987	120	41	the	the	DET
fcis-7987	120	42	heterogeneous	heterogeneous	ADJ
fcis-7987	120	43	network	network	NOUN
fcis-7987	120	44	traffic	traffic	NOUN
fcis-7987	120	45	data	datum	NOUN
fcis-7987	120	46	and	and	CCONJ
fcis-7987	120	47	rapidly	rapidly	ADV
fcis-7987	120	48	changing	change	VERB
fcis-7987	120	49	attack	attack	NOUN
fcis-7987	120	50	patterns	pattern	NOUN
fcis-7987	120	51	,	,	PUNCT
fcis-7987	120	52	which	which	PRON
fcis-7987	120	53	can	can	AUX
fcis-7987	120	54	meet	meet	VERB
fcis-7987	120	55	the	the	DET
fcis-7987	120	56	needs	need	NOUN
fcis-7987	120	57	of	of	ADP
fcis-7987	120	58	protecting	protect	VERB
fcis-7987	120	59	against	against	ADP
fcis-7987	120	60	the	the	DET
fcis-7987	120	61	endless	endless	ADJ
fcis-7987	120	62	stream	stream	NOUN
fcis-7987	120	63	of	of	ADP
fcis-7987	120	64	attack	attack	NOUN
fcis-7987	120	65	methods	method	NOUN
fcis-7987	120	66	in	in	ADP
fcis-7987	120	67	the	the	DET
fcis-7987	120	68	current	current	ADJ
fcis-7987	120	69	network	network	NOUN
fcis-7987	120	70	environment	environment	NOUN
fcis-7987	120	71	.	.	PUNCT
fcis-7987	121	1	therefore	therefore	ADV
fcis-7987	121	2	,	,	PUNCT
fcis-7987	121	3	combining	combine	VERB
fcis-7987	121	4	intrusion	intrusion	NOUN
fcis-7987	121	5	detection	detection	NOUN
fcis-7987	121	6	systems	system	NOUN
fcis-7987	121	7	with	with	ADP
fcis-7987	121	8	deep	deep	ADJ
fcis-7987	121	9	learning	learning	NOUN
fcis-7987	121	10	has	have	AUX
fcis-7987	121	11	become	become	VERB
fcis-7987	121	12	a	a	DET
fcis-7987	121	13	new	new	ADJ
fcis-7987	121	14	goal	goal	NOUN
fcis-7987	121	15	for	for	ADP
fcis-7987	121	16	researchers	researcher	NOUN
fcis-7987	121	17	in	in	ADP
fcis-7987	121	18	the	the	DET
fcis-7987	121	19	intrusion	intrusion	NOUN
fcis-7987	121	20	detection	detection	NOUN
fcis-7987	121	21	field	field	NOUN
fcis-7987	121	22	.	.	PUNCT
fcis-7987	122	1	based	base	VERB
fcis-7987	122	2	on	on	ADP
fcis-7987	122	3	an	an	DET
fcis-7987	122	4	understanding	understanding	NOUN
fcis-7987	122	5	of	of	ADP
fcis-7987	122	6	existing	exist	VERB
fcis-7987	122	7	deep	deep	ADJ
fcis-7987	122	8	learning	learning	NOUN
fcis-7987	122	9	-	-	PUNCT
fcis-7987	122	10	based	base	VERB
fcis-7987	122	11	intrusion	intrusion	NOUN
fcis-7987	122	12	detection	detection	NOUN
fcis-7987	122	13	methods	method	NOUN
fcis-7987	122	14	,	,	PUNCT
fcis-7987	122	15	particularly	particularly	ADV
fcis-7987	122	16	the	the	DET
fcis-7987	122	17	problems	problem	NOUN
fcis-7987	122	18	of	of	ADP
fcis-7987	122	19	using	use	VERB
fcis-7987	122	20	a	a	DET
fcis-7987	122	21	single	single	ADJ
fcis-7987	122	22	model	model	NOUN
fcis-7987	122	23	in	in	ADP
fcis-7987	122	24	intrusion	intrusion	NOUN
fcis-7987	122	25	detection	detection	NOUN
fcis-7987	122	26	methods	method	NOUN
fcis-7987	122	27	,	,	PUNCT
fcis-7987	122	28	this	this	DET
fcis-7987	122	29	paper	paper	NOUN
fcis-7987	122	30	analyzes	analyze	VERB
fcis-7987	122	31	and	and	CCONJ
fcis-7987	122	32	proposes	propose	VERB
fcis-7987	122	33	a	a	DET
fcis-7987	122	34	novel	novel	ADJ
fcis-7987	122	35	composite	composite	ADJ
fcis-7987	122	36	model	model	NOUN
fcis-7987	122	37	-	-	PUNCT
fcis-7987	122	38	based	base	VERB
fcis-7987	122	39	intrusion	intrusion	NOUN
fcis-7987	122	40	detection	detection	NOUN
fcis-7987	122	41	method	method	NOUN
fcis-7987	122	42	and	and	CCONJ
fcis-7987	122	43	applies	apply	VERB
fcis-7987	122	44	it	it	PRON
fcis-7987	122	45	to	to	ADP
fcis-7987	122	46	the	the	DET
fcis-7987	122	47	intrusion	intrusion	NOUN
fcis-7987	122	48	detection	detection	NOUN
fcis-7987	122	49	field	field	NOUN
fcis-7987	122	50	,	,	PUNCT
fcis-7987	122	51	verifying	verify	VERB
fcis-7987	122	52	its	its	PRON
fcis-7987	122	53	feasibility	feasibility	NOUN
fcis-7987	122	54	and	and	CCONJ
fcis-7987	122	55	effectiveness	effectiveness	NOUN
fcis-7987	122	56	through	through	ADP
fcis-7987	122	57	experiments	experiment	NOUN
fcis-7987	122	58	.	.	PUNCT
fcis-7987	123	1	references	reference	NOUN
fcis-7987	123	2	[	[	X
fcis-7987	123	3	1	1	NUM
fcis-7987	123	4	]	]	PUNCT
fcis-7987	123	5	yang	yang	PROPN
fcis-7987	123	6	l	l	PROPN
fcis-7987	123	7	,	,	PUNCT
fcis-7987	123	8	shami	shami	PROPN
fcis-7987	123	9	a.	a.	PROPN
fcis-7987	123	10	a	a	DET
fcis-7987	123	11	transfer	transfer	NOUN
fcis-7987	123	12	learning	learning	NOUN
fcis-7987	123	13	and	and	CCONJ
fcis-7987	123	14	optimized	optimize	VERB
fcis-7987	123	15	cnn	cnn	PROPN
fcis-7987	123	16	based	base	VERB
fcis-7987	123	17	intrusion	intrusion	NOUN
fcis-7987	123	18	detection	detection	NOUN
fcis-7987	123	19	system	system	NOUN
fcis-7987	123	20	for	for	ADP
fcis-7987	123	21	internet	internet	NOUN
fcis-7987	123	22	of	of	ADP
fcis-7987	123	23	vehicles[j	vehicles[j	NOUN
fcis-7987	123	24	]	]	PUNCT
fcis-7987	123	25	.	.	PUNCT
fcis-7987	124	1	arxiv	arxiv	PROPN
fcis-7987	124	2	preprint	preprint	VERB
fcis-7987	124	3	arxiv:2201.11812	arxiv:2201.11812	NOUN
fcis-7987	124	4	,	,	PUNCT
fcis-7987	124	5	2022	2022	NUM
fcis-7987	124	6	.	.	PUNCT
fcis-7987	125	1	[	[	X
fcis-7987	125	2	2	2	NUM
fcis-7987	125	3	]	]	X
fcis-7987	125	4	xiao	xiao	PROPN
fcis-7987	125	5	y	y	PROPN
fcis-7987	125	6	,	,	PUNCT
fcis-7987	125	7	xiao	xiao	PROPN
fcis-7987	125	8	x.	x.	PROPN
fcis-7987	125	9	an	an	DET
fcis-7987	125	10	intrusion	intrusion	NOUN
fcis-7987	125	11	detection	detection	NOUN
fcis-7987	125	12	system	system	NOUN
fcis-7987	125	13	based	base	VERB
fcis-7987	125	14	on	on	ADP
fcis-7987	125	15	a	a	DET
fcis-7987	125	16	simplified	simplified	ADJ
fcis-7987	125	17	residual	residual	ADJ
fcis-7987	125	18	network[j	network[j	NOUN
fcis-7987	125	19	]	]	PUNCT
fcis-7987	125	20	.	.	PUNCT
fcis-7987	126	1	information,2019	information,2019	PROPN
fcis-7987	126	2	,	,	PUNCT
fcis-7987	126	3	10(11	10(11	NUM
fcis-7987	126	4	):	):	PUNCT
fcis-7987	126	5	356	356	NUM
fcis-7987	126	6	.	.	PUNCT
fcis-7987	127	1	[	[	X
fcis-7987	127	2	3	3	X
fcis-7987	127	3	]	]	PUNCT
fcis-7987	127	4	yu	yu	PROPN
fcis-7987	127	5	y	y	PROPN
fcis-7987	127	6	,	,	PUNCT
fcis-7987	127	7	liu	liu	PROPN
fcis-7987	127	8	g	g	PROPN
fcis-7987	127	9	,	,	PUNCT
fcis-7987	127	10	yan	yan	PROPN
fcis-7987	127	11	h	h	PROPN
fcis-7987	127	12	,	,	PUNCT
fcis-7987	127	13	et	et	PROPN
fcis-7987	127	14	al	al	PROPN
fcis-7987	127	15	.	.	PUNCT
fcis-7987	127	16	attention	attention	NOUN
fcis-7987	127	17	-	-	PUNCT
fcis-7987	127	18	based	base	VERB
fcis-7987	127	19	bilstm	bilstm	NOUN
fcis-7987	127	20	model	model	NOUN
fcis-7987	127	21	for	for	ADP
fcis-7987	127	22	anomalous	anomalous	ADJ
fcis-7987	127	23	http	http	NOUN
fcis-7987	127	24	traffic	traffic	NOUN
fcis-7987	127	25	detection[c]//2018	detection[c]//2018	PROPN
fcis-7987	127	26	15th	15th	ADJ
fcis-7987	127	27	international	international	ADJ
fcis-7987	127	28	conference	conference	NOUN
fcis-7987	127	29	on	on	ADP
fcis-7987	127	30	service	service	NOUN
fcis-7987	127	31	systems	system	NOUN
fcis-7987	127	32	and	and	CCONJ
fcis-7987	127	33	service	service	NOUN
fcis-7987	127	34	management	management	NOUN
fcis-7987	127	35	,	,	PUNCT
fcis-7987	127	36	2018	2018	NUM
fcis-7987	127	37	:	:	PUNCT
fcis-7987	127	38	1	1	NUM
fcis-7987	127	39	-	-	SYM
fcis-7987	127	40	6	6	NUM
fcis-7987	127	41	.	.	PUNCT
fcis-7987	128	1	[	[	X
fcis-7987	128	2	4	4	X
fcis-7987	128	3	]	]	PUNCT
fcis-7987	128	4	bedi	bedi	PROPN
fcis-7987	128	5	p	p	PROPN
fcis-7987	128	6	,	,	PUNCT
fcis-7987	128	7	gupta	gupta	PROPN
fcis-7987	128	8	n	n	CCONJ
fcis-7987	128	9	,	,	PUNCT
fcis-7987	128	10	jindal	jindal	PROPN
fcis-7987	128	11	v.	v.	ADP
fcis-7987	128	12	siam	siam	PROPN
fcis-7987	128	13	-	-	PUNCT
fcis-7987	128	14	ids	ids	NOUN
fcis-7987	128	15	:	:	PUNCT
fcis-7987	128	16	handling	handle	VERB
fcis-7987	128	17	class	class	NOUN
fcis-7987	128	18	imbalance	imbalance	NOUN
fcis-7987	128	19	problem	problem	NOUN
fcis-7987	128	20	in	in	ADP
fcis-7987	128	21	intrusion	intrusion	NOUN
fcis-7987	128	22	detection	detection	NOUN
fcis-7987	128	23	systems	system	NOUN
fcis-7987	128	24	using	use	VERB
fcis-7987	128	25	siamese	siamese	ADJ
fcis-7987	128	26	neural	neural	ADJ
fcis-7987	128	27	network[j].procedia	network[j].procedia	PROPN
fcis-7987	128	28	computer	computer	NOUN
fcis-7987	128	29	science	science	NOUN
fcis-7987	128	30	,	,	PUNCT
fcis-7987	128	31	2020	2020	NUM
fcis-7987	128	32	,	,	PUNCT
fcis-7987	128	33	171	171	NUM
fcis-7987	128	34	:	:	PUNCT
fcis-7987	128	35	780	780	NUM
fcis-7987	128	36	-	-	SYM
fcis-7987	128	37	789	789	NUM
fcis-7987	128	38	.	.	PUNCT
fcis-7987	128	39	26	26	NUM
fcis-7987	129	1	[	[	SYM
fcis-7987	129	2	5	5	NUM
fcis-7987	129	3	]	]	SYM
fcis-7987	129	4	li	li	PROPN
fcis-7987	129	5	chuan	chuan	PROPN
fcis-7987	129	6	.	.	PUNCT
fcis-7987	130	1	research	research	NOUN
fcis-7987	130	2	and	and	CCONJ
fcis-7987	130	3	implementation	implementation	NOUN
fcis-7987	130	4	of	of	ADP
fcis-7987	130	5	intrusion	intrusion	NOUN
fcis-7987	130	6	detection	detection	NOUN
fcis-7987	130	7	based	base	VERB
fcis-7987	130	8	on	on	ADP
fcis-7987	130	9	generative	generative	ADJ
fcis-7987	130	10	adversarial	adversarial	ADJ
fcis-7987	130	11	networks	network	NOUN
fcis-7987	131	1	[	[	X
fcis-7987	131	2	d	d	X
fcis-7987	131	3	]	]	X
fcis-7987	131	4	.	.	PUNCT
fcis-7987	132	1	north	north	PROPN
fcis-7987	132	2	china	china	PROPN
fcis-7987	132	3	electric	electric	PROPN
fcis-7987	132	4	power	power	PROPN
fcis-7987	132	5	university	university	PROPN
fcis-7987	132	6	(	(	PUNCT
fcis-7987	132	7	beijing),2022.doi:10.27140	beijing),2022.doi:10.27140	PROPN
fcis-7987	132	8	/d.cnki.ghbbu	/d.cnki.ghbbu	PUNCT
fcis-7987	132	9	.	.	PUNCT
fcis-7987	133	1	2022.000269	2022.000269	NOUN
fcis-7987	133	2	.	.	PUNCT
fcis-7987	134	1	[	[	X
fcis-7987	134	2	6	6	NUM
fcis-7987	134	3	]	]	SYM
fcis-7987	134	4	fu	fu	PROPN
fcis-7987	134	5	y	y	PROPN
fcis-7987	134	6	,	,	PUNCT
fcis-7987	134	7	du	du	PROPN
fcis-7987	134	8	y	y	PROPN
fcis-7987	134	9	,	,	PUNCT
fcis-7987	134	10	cao	cao	PROPN
fcis-7987	134	11	z	z	PROPN
fcis-7987	134	12	,	,	PUNCT
fcis-7987	134	13	et	et	PROPN
fcis-7987	134	14	al	al	PROPN
fcis-7987	134	15	.	.	PUNCT
fcis-7987	135	1	a	a	DET
fcis-7987	135	2	deep	deep	ADJ
fcis-7987	135	3	learning	learning	NOUN
fcis-7987	135	4	model	model	NOUN
fcis-7987	135	5	for	for	ADP
fcis-7987	135	6	network	network	NOUN
fcis-7987	135	7	intrusion	intrusion	NOUN
fcis-7987	135	8	detection	detection	NOUN
fcis-7987	135	9	with	with	ADP
fcis-7987	135	10	imbalanced	imbalanced	ADJ
fcis-7987	135	11	data[j	data[j	NOUN
fcis-7987	135	12	]	]	X
fcis-7987	135	13	.	.	PUNCT
fcis-7987	136	1	electronics	electronic	NOUN
fcis-7987	136	2	,	,	PUNCT
fcis-7987	136	3	2022	2022	NUM
fcis-7987	136	4	,	,	PUNCT
fcis-7987	136	5	11(6	11(6	NUM
fcis-7987	136	6	):	):	PUNCT
fcis-7987	136	7	898	898	NUM
fcis-7987	136	8	.	.	PUNCT
fcis-7987	137	1	[	[	X
fcis-7987	137	2	7	7	X
fcis-7987	137	3	]	]	X
fcis-7987	137	4	staudemeyer	staudemeyer	NOUN
fcis-7987	137	5	r	r	NOUN
fcis-7987	137	6	c	c	PROPN
fcis-7987	137	7	.applying	.applye	VERB
fcis-7987	137	8	long	long	ADJ
fcis-7987	137	9	short	short	ADJ
fcis-7987	137	10	-	-	PUNCT
fcis-7987	137	11	term	term	NOUN
fcis-7987	137	12	memory	memory	NOUN
fcis-7987	137	13	recurrent	recurrent	NOUN
fcis-7987	137	14	neural	neural	ADJ
fcis-7987	137	15	networks	network	NOUN
fcis-7987	137	16	to	to	PART
fcis-7987	137	17	intrusion	intrusion	NOUN
fcis-7987	137	18	detection[j	detection[j	PROPN
fcis-7987	137	19	]	]	PUNCT
fcis-7987	137	20	.	.	PUNCT
fcis-7987	138	1	south	south	ADJ
fcis-7987	138	2	african	african	ADJ
fcis-7987	138	3	computer	computer	NOUN
fcis-7987	138	4	journal	journal	NOUN
fcis-7987	138	5	,	,	PUNCT
fcis-7987	138	6	2015,(1):136	2015,(1):136	NOUN
fcis-7987	138	7	-	-	PUNCT
fcis-7987	138	8	154	154	NUM
fcis-7987	138	9	.	.	PUNCT
fcis-7987	139	1	[	[	X
fcis-7987	139	2	8	8	NUM
fcis-7987	139	3	]	]	X
fcis-7987	139	4	volodymyr	volodymyr	PROPN
fcis-7987	139	5	mnih	mnih	PROPN
fcis-7987	139	6	,	,	PUNCT
fcis-7987	139	7	nicolas	nicolas	PROPN
fcis-7987	139	8	heess	heess	PROPN
fcis-7987	139	9	,	,	PUNCT
fcis-7987	139	10	alex	alex	PROPN
fcis-7987	139	11	graves	graves	PROPN
fcis-7987	139	12	,	,	PUNCT
fcis-7987	139	13	koray	koray	PROPN
fcis-7987	139	14	kavukcuoglu	kavukcuoglu	PROPN
fcis-7987	139	15	.	.	PUNCT
fcis-7987	140	1	recurrent	recurrent	ADJ
fcis-7987	140	2	models	model	NOUN
fcis-7987	140	3	of	of	ADP
fcis-7987	140	4	visual	visual	ADJ
fcis-7987	140	5	attention	attention	NOUN
fcis-7987	140	6	.	.	PUNCT
fcis-7987	141	1	arxiv	arxiv	PROPN
fcis-7987	141	2	.	.	PUNCT
fcis-7987	142	1	preprint	preprint	PROPN
fcis-7987	142	2	arxiv:1406.6247v1.2014	arxiv:1406.6247v1.2014	PROPN
fcis-7987	142	3	.	.	PUNCT
fcis-7987	143	1	[	[	X
fcis-7987	143	2	9	9	X
fcis-7987	143	3	]	]	X
fcis-7987	143	4	mhaskar	mhaskar	PROPN
fcis-7987	143	5	h	h	PROPN
fcis-7987	143	6	n	n	CCONJ
fcis-7987	143	7	,	,	PUNCT
fcis-7987	143	8	micchelli	micchelli	NOUN
fcis-7987	143	9	c	c	NOUN
fcis-7987	143	10	a.	a.	NOUN
fcis-7987	143	11	how	how	SCONJ
fcis-7987	143	12	to	to	PART
fcis-7987	143	13	choose	choose	VERB
fcis-7987	143	14	an	an	DET
fcis-7987	143	15	activation	activation	NOUN
fcis-7987	143	16	function[j].advances	function[j].advance	NOUN
fcis-7987	143	17	in	in	ADP
fcis-7987	143	18	neural	neural	ADJ
fcis-7987	143	19	information	information	NOUN
fcis-7987	143	20	processing	process	VERB
fcis-7987	143	21	systems,1994	systems,1994	NOUN
fcis-7987	143	22	:	:	PUNCT
fcis-7987	143	23	319	319	NUM
fcis-7987	143	24	-	-	SYM
fcis-7987	143	25	326	326	NUM
fcis-7987	143	26	.	.	PUNCT
fcis-7987	144	1	[	[	X
fcis-7987	144	2	10	10	NUM
fcis-7987	144	3	]	]	PUNCT
fcis-7987	144	4	he	he	PRON
fcis-7987	144	5	k	k	PROPN
fcis-7987	144	6	,	,	PUNCT
fcis-7987	144	7	zhang	zhang	PROPN
fcis-7987	144	8	x	x	PROPN
fcis-7987	144	9	,	,	PUNCT
fcis-7987	144	10	ren	ren	PROPN
fcis-7987	144	11	s	s	PART
fcis-7987	144	12	,	,	PUNCT
fcis-7987	144	13	et	et	PROPN
fcis-7987	144	14	al	al	PROPN
fcis-7987	144	15	.	.	PUNCT
fcis-7987	144	16	identity	identity	NOUN
fcis-7987	144	17	mappings	mapping	NOUN
fcis-7987	144	18	in	in	ADP
fcis-7987	144	19	deep	deep	ADJ
fcis-7987	144	20	residual	residual	ADJ
fcis-7987	144	21	networks[c]//	networks[c]//	PROPN
fcis-7987	144	22	european	european	ADJ
fcis-7987	144	23	conference	conference	NOUN
fcis-7987	144	24	on	on	ADP
fcis-7987	144	25	computer	computer	NOUN
fcis-7987	144	26	vision	vision	NOUN
fcis-7987	144	27	.	.	PUNCT
fcis-7987	145	1	springer	springer	NOUN
fcis-7987	145	2	,	,	PUNCT
fcis-7987	145	3	cham	cham	PROPN
fcis-7987	145	4	,	,	PUNCT
fcis-7987	145	5	2016	2016	NUM
fcis-7987	145	6	.	.	PUNCT
fcis-7987	146	1	[	[	X
fcis-7987	146	2	11	11	NUM
fcis-7987	146	3	]	]	X
fcis-7987	146	4	luong	luong	PROPN
fcis-7987	146	5	m	m	PROPN
fcis-7987	146	6	t	t	PROPN
fcis-7987	146	7	,	,	PUNCT
fcis-7987	146	8	pham	pham	PROPN
fcis-7987	146	9	h	h	PROPN
fcis-7987	146	10	,	,	PUNCT
fcis-7987	146	11	manning	man	VERB
fcis-7987	146	12	c	c	X
fcis-7987	146	13	d.	d.	PROPN
fcis-7987	146	14	effective	effective	ADJ
fcis-7987	146	15	approaches	approach	NOUN
fcis-7987	146	16	to	to	ADP
fcis-7987	146	17	attention	attention	NOUN
fcis-7987	146	18	-	-	PUNCT
fcis-7987	146	19	based	base	VERB
fcis-7987	146	20	neural	neural	ADJ
fcis-7987	146	21	machine	machine	NOUN
fcis-7987	146	22	translation[j	translation[j	PROPN
fcis-7987	146	23	]	]	PUNCT
fcis-7987	146	24	.	.	PUNCT
fcis-7987	147	1	arxiv	arxiv	PROPN
fcis-7987	147	2	preprint	preprint	NOUN
fcis-7987	147	3	arxiv:1508.04025	arxiv:1508.04025	NUM
fcis-7987	147	4	,	,	PUNCT
fcis-7987	147	5	2015	2015	NUM
fcis-7987	147	6	.	.	PUNCT
fcis-7987	148	1	[	[	X
fcis-7987	148	2	12	12	NUM
fcis-7987	148	3	]	]	X
fcis-7987	148	4	mnih	mnih	PROPN
fcis-7987	148	5	v	v	PROPN
fcis-7987	148	6	,	,	PUNCT
fcis-7987	148	7	heess	heess	NOUN
fcis-7987	148	8	n	n	CCONJ
fcis-7987	148	9	,	,	PUNCT
fcis-7987	148	10	graves	grave	VERB
fcis-7987	148	11	a.	a.	NOUN
fcis-7987	148	12	recurrent	recurrent	PROPN
fcis-7987	148	13	models	model	NOUN
fcis-7987	148	14	of	of	ADP
fcis-7987	148	15	visual	visual	ADJ
fcis-7987	148	16	attention[j	attention[j	PROPN
fcis-7987	148	17	]	]	PUNCT
fcis-7987	148	18	.	.	PUNCT
fcis-7987	149	1	advances	advance	NOUN
fcis-7987	149	2	in	in	ADP
fcis-7987	149	3	neural	neural	ADJ
fcis-7987	149	4	information	information	NOUN
fcis-7987	149	5	processing	processing	NOUN
fcis-7987	149	6	systems	system	NOUN
fcis-7987	149	7	,	,	PUNCT
fcis-7987	149	8	2014	2014	NUM
fcis-7987	149	9	,	,	PUNCT
fcis-7987	149	10	27	27	NUM
fcis-7987	149	11	.	.	PUNCT
fcis-7987	150	1	[	[	X
fcis-7987	150	2	13	13	NUM
fcis-7987	150	3	]	]	X
fcis-7987	150	4	olanow	olanow	NOUN
fcis-7987	150	5	c	c	PROPN
fcis-7987	150	6	w	w	PROPN
fcis-7987	150	7	,	,	PUNCT
fcis-7987	150	8	koller	koller	PROPN
fcis-7987	150	9	w	w	PROPN
fcis-7987	150	10	c	c	PROPN
fcis-7987	150	11	.	.	PUNCT
fcis-7987	151	1	an	an	DET
fcis-7987	151	2	algorithm	algorithm	NOUN
fcis-7987	151	3	(	(	PUNCT
fcis-7987	151	4	decision	decision	NOUN
fcis-7987	151	5	tree	tree	NOUN
fcis-7987	151	6	)	)	PUNCT
fcis-7987	151	7	for	for	ADP
fcis-7987	151	8	the	the	DET
fcis-7987	151	9	management	management	NOUN
fcis-7987	151	10	of	of	ADP
fcis-7987	151	11	parkinson	parkinson	NOUN
fcis-7987	151	12	's	's	PART
fcis-7987	151	13	disease	disease	NOUN
fcis-7987	151	14	:	:	PUNCT
fcis-7987	151	15	treatment	treatment	NOUN
fcis-7987	151	16	guidelines[j	guidelines[j	PROPN
fcis-7987	151	17	]	]	PUNCT
fcis-7987	151	18	.	.	PUNCT
fcis-7987	152	1	neurology	neurology	PROPN
fcis-7987	152	2	,	,	PUNCT
fcis-7987	152	3	1998	1998	NUM
fcis-7987	152	4	,	,	PUNCT
fcis-7987	152	5	50(3	50(3	NUM
fcis-7987	152	6	suppl	suppl	NOUN
fcis-7987	152	7	3):s1	3):s1	NUM
fcis-7987	152	8	.	.	PUNCT
fcis-7987	153	1	[	[	X
fcis-7987	153	2	14	14	NUM
fcis-7987	153	3	]	]	PUNCT
fcis-7987	153	4	alrawashdeh	alrawashdeh	NOUN
fcis-7987	153	5	k	k	NOUN
fcis-7987	153	6	,	,	PUNCT
fcis-7987	153	7	purdy	purdy	PROPN
fcis-7987	153	8	c	c	NOUN
fcis-7987	153	9	.	.	PUNCT
fcis-7987	154	1	toward	toward	ADP
fcis-7987	154	2	an	an	DET
fcis-7987	154	3	online	online	ADJ
fcis-7987	154	4	anomaly	anomaly	NOUN
fcis-7987	154	5	intrusion	intrusion	NOUN
fcis-7987	154	6	detection	detection	NOUN
fcis-7987	154	7	systen	systen	VERB
fcis-7987	154	8	based	base	VERB
fcis-7987	154	9	on	on	ADP
fcis-7987	154	10	deep	deep	ADJ
fcis-7987	154	11	learning[c].//2016	learning[c].//2016	PROPN
fcis-7987	154	12	15th	15th	ADJ
fcis-7987	154	13	ieee	ieee	PROPN
fcis-7987	154	14	international	international	ADJ
fcis-7987	154	15	conference	conference	NOUN
fcis-7987	154	16	on	on	ADP
fcis-7987	154	17	machine	machine	NOUN
fcis-7987	154	18	learning	learning	NOUN
fcis-7987	154	19	and	and	CCONJ
fcis-7987	154	20	applications(icmla).ieee,2016	applications(icmla).ieee,2016	PROPN
fcis-7987	154	21	.	.	PUNCT