Artificial neural network-based intrusion detection system using multi-objective genetic algorithm Online publication date: Wed, 09-Aug-2023
by N.D. Patel; B.M. Mehtre; Rajeev Wankar
International Journal of Information and Computer Security (IJICS), Vol. 21, No. 3/4, 2023
Abstract: With recent advances in cyber-attacks, traditional rule-based intrusion detection systems are not adequate to meet the present-day challenge. Recently machine learning-based intrusion detection system (IDS) has been proposed to detect such advanced/unknown cyber-attacks. The performance of such machine learning-based IDS largely depends upon the feature set used. Generally, using more features increases the accuracy of attack detection and increases detection time. This paper proposes a new network intrusion detection system based on an artificial neural network (ANN), which uses a multi-objective genetic algorithm to satisfy the requirements: accuracy of attack detection and faster response. The performance of the proposed method is tested by using the KDD'99, NSL-KDD, and CIC-IDS-2017 datasets. The results show that the performance of the proposed method is better than the existing methods. Besides, the new process provides a trade-off on the number of features used vs. accuracy and time for detection.
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