Anomaly Detection Model Based on Bio-Inspired Algorithm and Independent Component Analysis

With the advent and explosive growth of the global Internet and electronic commerce environments, adaptive/automatic network/service intrusion and anomaly detection in wide area data networks and e-commerce infrastructures is fast gaining critical research and practical importance. In this paper we present independent component analysis (ICA) based feature selection heuristics approach to data clustering. Independent component analysis is used to initially create raw clusters and then these clusters are refined using parallel artificial immune recognition system (AIRS). AIRS that has been developed as an immune system techniques. The algorithm uses artificial immune system (AIS) principle to find good partitions of the data. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose parallel artificial immune recognition system (AIRS)) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on knowledge discovery and data mining-(KDDCup 1999)