@article {Ezzarii2020,
	title = {Epigenetic algorithm-based detection technique for network attacks},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {cited By 2},
	abstract = {Nowadays, the cybersecurity issue involves new strategies to protect against advanced threats and unknown attacks. Intrusion detection system (IDS) is considered a robust system dealing with attacks detection, particularly unknown attacks and anomalies. Several IDS-based algorithms have been recently inspected in the literature, among them the well-known strengthen algorithms, i.e. Genetic algorithm (GA). Moreover, Epigenetic-based algorithm (EGA) is known as an improved version of GA ensuring high performance with reduced computational complexity. Its main goal is to converge within a short time towards an optimal solution by acting on genetic operators, namely mutation and crossover. In this article, we propose a new classifier based on EGA for IDS. Especially, based on a database of network traffics, EGA is applied to classify attacks. The results, performed through EGA simulation, show that the performance of the proposed technique outperforms the ones of GA classifier by obtaining a high detection rate up to 98\% and a faster processing time than that of GA and other algorithms that we have compared in this article. {\textcopyright} 2020 BMJ Publishing Group. All rights reserved.},
	keywords = {Classification (of information), Computer crime, Genetic algorithms, Genetic operators, High detection rate, Intrusion detection, Intrusion Detection Systems, Network attack, Network security, Network traffic, Optimal solutions, Processing time, Unknown attacks},
	doi = {10.1109/ACCESS.2020.3035250},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097226994\&doi=10.1109\%2fACCESS.2020.3035250\&partnerID=40\&md5=2b4d9568d49e06ee5d704619bbb792f2},
	author = {Ezzarii, M. and El Ghazi, H. and El Ghazi, H. and El Bouanani, F.}
}
