  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, self-
monitoring and distributed nature of Human Immune Systems. Whereas 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
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.
