A real time anomaly detection system based on probabilistic artificial immune based algorithm

Authors:
Mahdi Mohammadi;Ahmad Akbari;Bijan Raahemi;Babak Nassersharif
Affiliations:
Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran;Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran;University of Ottawa, E., Ottawa, ON, Canada;Electrical and Computer Engineering Department, K.N. Toosi University of Technology, Iran
Venue:
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Year:
2012

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Abstract

Artificial Immune System (AIS)-based evolutionary algorithms combine rules and randomness to solve optimization and classification problems. Due to their capability in identifying self and non self samples, they have also gained attention in intrusion detection systems. In this paper, we propose a real-time AIS-based anomoly detection algorithm for intrusion detection. The most important features of the proposed method are its high detection rate, low false alarm, low computational complexity, and real-time response to the incoming samples. We compare our proposed method with several well-known anomaly detection algorithms on various datasets. We demonstrate that the proposed method performs the best among others in terms of false alarm, detection rate and time response.