@conference {Rachidi202213,
	title = {Network intrusion detection using Machine Learning approach},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2022},
	note = {cited By 0},
	pages = {13-17},
	abstract = {Today, intrusion detection has become an active research area. Due to the rapidly increasing number of intrusion variants, intrusion detection system analyses and notifies the activities of users as normal (or) anomaly. In our paper, we built a model of intrusion detection system applied to the NSL-KDD data set using different supervised classifiers such as KNN and Na{\"\i}ve Bayes. We also proposed two algorithms for multi-classification based on the Random Forest (RF) which is an ensemble classifier and KNN. Then we used the K-folds method to evaluate and validate our model. To evaluate the performances, we realized experiments on NSL-KDD data set. The result shows that the second proposed algorithm is efficient with high accuracy and time optimization. {\textcopyright} 2022 ACM.},
	keywords = {Barium compounds, Classification (of information), Computer crime, data set, Decision trees, Intrusion detection, Intrusion Detection Systems, Intrusion-Detection, Machine learning, Machine learning approaches, Multi-classification, Network intrusion detection, NSL-KDD, Random forests, Research areas, Sodium compounds, Supervised classifiers},
	doi = {10.1145/3551690.3551693},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139141127\&doi=10.1145\%2f3551690.3551693\&partnerID=40\&md5=e47a5f8038487b706673543f7fdf2de4},
	author = {Rachidi, Z. and Chougdali, K. and Kobbane, A. and Ben-Othman, J.}
}
@article {Karbal2020157,
	title = {A Comparison of Different Machine Learning Algorithms for Intrusion Detection},
	journal = {Communications in Computer and Information Science},
	volume = {1264},
	year = {2020},
	note = {cited By 0},
	pages = {157-169},
	abstract = {With the rapid development of the internet, intrusion detection became one of the major research problems in computer security. Many Intrusion Detection Systems (IDS) use data mining algorithms for classifying network traffic data and detecting different security violations. In this paper, we present some of the datasets and methods employed with the focus on network anomaly detection. We compare different machine learning techniques used in the latest research carried out for developing network intrusion detection systems. We also present an overview of some deep learning methodologies and their application for IDS purposes. The primary objective of this survey is to provide with a researcher, the state of the artwork already performed in this field of research. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Anomaly detection, Classification (of information), Computer crime, Data communication systems, Data mining, Data mining algorithm, Deep learning, Intrusion detection, Intrusion Detection Systems, Learning algorithms, Learning systems, Machine learning techniques, Network anomaly detection, Network intrusion detection systems, Network security, Primary objective, Research problems, Security violations},
	doi = {10.1007/978-3-030-61143-9_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097408678\&doi=10.1007\%2f978-3-030-61143-9_13\&partnerID=40\&md5=3725b891261e386c6df84eebcbc0e514},
	author = {Karbal, B. and Romadi, R.}
}
@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.}
}
