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The analogy between immune systems and intrusion detection systems encourage the use of artificial immune systems for anomaly detection in computer networks. This paper describes a technique of applying artificial immune system along with genetic algorithm to develop an intrusion detection system. Far from developing primary immune response, as most of the related works do, it attempts to evolve this primary immune response to a secondary immune response using the concept of memory cells prevalent in natural immune systems. A genetic algorithm using genetic operators- selection, cloning, crossover and mutation- facilitates this. Memory cells formed enable faster detection of already encountered attacks. These memory cells, being highly random in nature, are dependent on the evolution of the detectors and guarantee greater immunity from anomalies and attacks. The fact that the whole procedure is enveloped in the concepts of approximate binding and memory cells of lightweight of natural immune systems makes this system reliable, robust and quick responding.
Date of Conference: 26-28 June 2008