Network intrusion detection using Machine Learning approach

Titre	Network intrusion detection using Machine Learning approach
Publication Type	Conference Paper
Year of Publication	2022
Authors	Rachidi, Z, Chougdali, K, Kobbane, A, Ben-Othman, J
Conference Name	ACM International Conference Proceeding Series
Mots-clés	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
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 Naïve 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. © 2022 ACM.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139141127&doi=10.1145%2f3551690.3551693&partnerID=40&md5=e47a5f8038487b706673543f7fdf2de4
DOI	10.1145/3551690.3551693