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A number of works in the field of intrusion detection have been based onArtificial Immune System and Soft Computing. Artificial Immune System basedapproaches attempt to leverage the adaptability, error tolerance, self-monitoring and distributed nature of Human Immune Systems. Whereas SoftComputing based approaches are instrumental in developing fuzzy rule basedsystems for detecting intrusions. They are computationally intensive and applymachine learning (both supervised and unsupervised) techniques to detectintrusions in a given system. A combination of these two approaches couldprovide significant advantages for intrusion detection. In this paper weattempt to leverage the adaptability of Artificial Immune System and thecomputation intensive nature of Soft Computing to develop a system that caneffectively detect intrusions in a given network.

A Hybrid Approach Towards Intrusion Detection Based on Artificial Immune System and Soft Computing