AIS : A Computational Approach for Network Intrusion Detection System

Abstract

Computer systems today can be seen as a problem of pattern classification, the system must deal with some intrinsic characteristics that make it very difficult to detect intrusions directly using classical pattern recognition methods. For example, normal and anomalous states are distinguished using features that are multi-dimensional, and there is extreme asymmetry and high overlap in the amount of data available for these two systems of states. Furthermore, the patterns involved cannot be recognized by linear methods without kernel maps. The natural human immune system faces the same difficulties, but successfully protects the body against a vast variety of foreign pathogens. It is a self-adaptive and self-learning classifier that can recognize and classify threats by learning, long-term memory, and association. We have adapted the mechanisms of the human immune system to construct an intrusion detection system to protect computer networks.

Keywords: Artificial immune system, Network intrusion detection, Classification, Immunology.