An Adaptive Intrusion Detection System Using Artificial Immune Systems and Clonal Selection Algorithms

International Journal of Computer Science and Technology | Vol. 13, No. 3, March 2011 | pp. 15–21
DOI: 10.46882/2011/IJCST/000109

Research Article

Title: An Adaptive Intrusion Detection System Using Artificial Immune Systems and Clonal Selection Algorithms

Names of Authors: Christopher L. Davis¹, Zainab M. Al-Balushi²

Authors’ Affiliations: ¹Department of Computer Science, University of Toronto, Toronto, Canada; ²Information Security Department, Sultan Qaboos University, Muscat, Oman

Abstract: Cyber infrastructures suffer continuously from zero-day threats and network intrusions that easily bypass signature-based firewall systems. This paper describes an Adaptive Intrusion Detection System (AIDS) modeled after biological Artificial Immune Systems (AIS), specifically implementing the Clonal Selection Algorithm (CSA). The system treats incoming network packets as antigens and constructs an evolving population of digital antibodies (detectors) designed to recognize anomalous communication profiles. An affinity maturation cycle optimizes these detectors through targeted mutations, ensuring rapid convergence toward novel intrusion footprints. The proposed model was validated using the standard KDD Cup 99 benchmark dataset. The experimental findings indicate a true positive anomaly detection rate of 97.4%, paired with a low false positive alert frequency of 0.65%. The system demonstrated autonomous self-learning capabilities, discovering modified polymorphic network scans within 180 seconds of exposure without requiring administrative signature revisions. This framework exhibits strong computational resilience and high real-time throughput execution, making it suitable for deployment at the boundary routers of enterprise wide-area networks.

Keywords: Intrusion Detection System, Artificial Immune Systems, Clonal Selection, Cyber Security, Network Anomaly

Manuscript Timeline: Received October 10, 2010; Revised November 18, 2010; Accepted December 01, 2010; Published March 18, 2011.

Citation: Davis, L. C., & Al-Balushi, M. Z. (2011). An Adaptive Intrusion Detection System Using Artificial Immune Systems and Clonal Selection Algorithms. International Journal of Computer Science and Technology, 13(3), 15–21.

Subscribe to read the full article: https://www.internationalscholarsjournals.org/subscribe-to-read