id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-10369	Li, Xin; Huang, Hong; Yuan, Guotao; Wang, Zhaolian; Du, Rui	An Intrusion Detection Method based on Fusion Neural Network	2023	7	.pdf	application/pdf	4930	293	49	UNSW-NB15 contains a wider range of attack types and network traffic features, allowing for a more comprehensive evaluation of intrusion detection models. Insufficient feature learning from the minority class samples leads to lower detection rates for these classes, which affects the performance of intrusion detection models and may result in highly threatening attack behaviors being misclassified as normal behavior, thereby adversely affecting the devices.	cache/fcis-10369.pdf	txt/fcis-10369.txt