Int. J. Engg, Sc. & Mgmt. / Vol. II / Issue II / Jul-Dec 2012 ISSN: 2231-3273

A number of works in the field of intrusion detection have been based on Artificial Immune System and Soft Computing. Artificial-Immune-System-based approaches attempt to leverage the adaptability, error tolerance, selfmonitoring and distributed nature of Human Immune Systems. The Soft-Computing-based approaches are instrumental in developing fuzzy-rule-based systems for detecting intrusions. They are computationally intensive and apply machine learning (both supervised and unsupervised) techniques to detect the intrusions in a given system. A combination of these two approaches could provide significant advantages for intrusion detection. In this paper we attempt to leverage the adaptability of Artificial Immune System and the computation intensive nature of Soft Computing to develop a system that can effectively detect intrusions in a given network.