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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 algorithms, artificial neural networks, fuzzy logic, swarm intelligence, etc. Due to the high dimensionality of the exchanged data, applying those techniques will be extremely time consuming. Feature selection is needed to select the optimal subset of features that represents the entire dataset to increase the accuracy and the classification performance of the IDS. In this work, we apply …