Immune Detector Optimization Algorithm with Co-evolution and Monte Carlo

The detector which is devoted to detect the abnormal events in the immune-based instrusion detection system (IDS) is absolutely necessary. But, some problems in the detector set need to be solved before detection, and at the same time, the research in the security vulnerabilities detector optimization is important. In this paper, inspired by the species’ co-evolution in nature and the Monte Carlo method, An algorithm of immune detector optimization is presented: co-evolve among detector subsets, estimate the coverage rate by Monte Carlo to end the optimization. Getting a conclusion by the experimental tests is that the security holes can be fewer by the algorithm, and less detectors can be used to achieve more accurate coverage of non-self-space.