Intrusion Detection System Based On Immunological Theory

Recently, more and more attacks to the information systems appear and become much harder to resist than before. The attack methods have changed from the traditional local virus attacks to the distributed, high -speed spread network attacks. Because of the inherent defects, the intrusion detection technologies we now have are not efficient for this condition. Therefore, researchers use the metaphors taken from the biological immunological theory to make an improvement on intrusion detection system (IDS).Firstly, this dissertation introduces the gloss, function, modeling methods and classification of the IDS. A brief introduction about the intrusion detection technology is made, and we point out the developmental direction in which the IDS will follow in the future. After that, we specify the mechanism of how the immune system works and two immune-based algorithms are referred, which are clone selection algorithm and negative selection algorithm. Next we analyze an immune inspired distributed network intrusion detection model and discuss the problems occurred in the model. An immune-fuzzy classification method is presented, which can be applied to create the detectors in intrusion detection. Moreover we make an experiment to evaluate the performance of the approach and an analysis is made. Finally, we take LISYS, designed by the researchers of UNM, as an example to show how the immune system be simulated in the intrusion detection and give the drawbacks existing in the LISYS. Several advices are given to improve the LISYS.