Title: Artificial neural network-based intrusion detection system using multi-objective genetic algorithm
Authors: N.D. Patel; B.M. Mehtre; Rajeev Wankar
Addresses: Centre of Excellence in Cyber Security, Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India ' Centre of Excellence in Cyber Security, Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India ' School of Computer and Information Sciences (SCIS), University of Hyderabad (UoH), Hyderabad, India
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.
Keywords: intrusion detection system; IDS; advanced persistent threat; KDD'99; NSL-KDD; CIC-IDS-2017; feature selection; artificial neural network; ANN; multi-objective genetic algorithm.
International Journal of Information and Computer Security, 2023 Vol.21 No.3/4, pp.320 - 335
Received: 29 Jul 2021
Accepted: 14 Feb 2022
Published online: 09 Aug 2023 *