id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-2930	Bhuiyan, Mohammad Rakibul Islam ; Faraji, Mahfujur Rahman ; Tabassum, Mst. Nowshin ; Ghose, Provakar ; Sarbabidya, Sukanta ; Akter, Riva 	Leveraging Machine Learning for Cybersecurity: Techniques, Challenges, and Future Directions	2024	17	.pdf	application/pdf	9451	633	41	It Generated a significant advancement, extension buried of healthcare, finance, transportation and more unlocked up to date for research and innovation alongside ML. machine learning (ML) has numerous robust and statistical tactics for predictive motives, algorithms in general were supervised and unsupervised learning (Wang et al., 2023). By analyzing historical data, machine learning models can adapt to the constantly changing landscape of cyber threats, which increases the efficiency of intrusion detection and prevention (Gonaygunta, 2023).	cache/easat-2930.pdf	txt/easat-2930.txt