Open AccessFeature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm
Author(s) -
Atheer R. Muhsen,
Ghazwh G. Jumaa,
Nadia F. Al Bakri,
Ahmed T. Sadiq
Publication year - 2021
Publication title -
international journal of interactive mobile technologies
Language(s) - English
Resource type - Journals
ISSN - 1865-7923
DOI - 10.3991/ijim.v15i16.24173
Subject(s) - intrusion detection system , computer science , clan , feature selection , hacker , algorithm , selection (genetic algorithm) , network security , data mining , computer network , machine learning , computer security , sociology , anthropology
The task of network security is to keep services available at all times by dealing with hacker attacks. One of the mechanisms obtainable is the Intrusion Detection System (IDS) which is used to sense and classify any abnormal actions. Therefore, the IDS system should always be up-to-date with the latest hacker attack signatures to keep services confidential, safe, and available. IDS speed is a very important issue in addition to learning new attacks. A modified selection strategy based on features was proposed in this paper one of the important swarm intelligent algorithms is the Meerkat Clan Algorithm (MCA). Meerkat Clan Algorithm has good diversity solutions through its neighboring generation conduct and it was used to solve several problems. The proposed strategy benefitted from mutual information to increase the performance and decrease the consumed time. Two datasets (NSL-KDD & UNSW-NB15) for Network Intrusion Detection Systems (NIDS) have been used to verify the performance of the proposed algorithm. The experimental findings indicate that, compared to other approaches, the proposed algorithm produces good results in a minimum of time.