Intrusions constitute one of the main issues in computer network security. Through malicious
actions, hackers can have unauthorised access that compromises the integrity, the
confidentiality, and the availability of resources or services. Intrusion detection systems
(IDSs) have been developed to monitor and filter network activities by identifying attacks and
alerting network administrators. Different IDS approaches have emerged using data mining,
machine learning, statistical analysis, and artificial intelligence techniques such as genetic …

Feature selection is becoming a major preprocessing phase in which irrelevant and
redundant features are removed, while the more informative ones are retained. The datasets
used in intrusion detection systems contain many features. It is, therefore, necessary to apply
a feature selection step to improve the classification performance and reduce the
computation time. In this paper, we propose a multi-objective feature selection approach
based on NSGA-II and logistic regression in network intrusion detection. The proposed …