An Unsupervised Intrusion Detection Method Combined Clustering with Chaos Simulated Annealing

Keeping networks security has never been such an imperative task as today. Threats come from hardware failures, software flaws, tentative probing and malicious attacks. In this paper, a new detection method, Intrusion Detection based on Unsupervised Clustering and Chaos Simulated Annealing algorithm (IDCCSA), is proposed. As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of simulated annealing which is to find a near-optimal partitioning clustering, simulated annealing algorithm is proposed by incorporating chaos. Experiments with KDD cup 1999 show that the simulated annealing combined with chaos can effectively enhance the searching efficiency and greatly improve the detection quality.