Anomaly Detection Method Based on CSA-Based Unsupervised Fuzzy Clustering Algorithm
Xian Ji-qing · Beijing Youdian Xueyuan xuebao · 2005
A novel intrusion detection method based on clonal selection algorithm (CSA)-based unsupervised fuzzy clustering algorithm was presented for solving the problem of fuzzy k-means algorithm which is much more sensitive to the initialization and is easy to fall into local optimization. With the method, the global optimal clustering with clonal operator which combines the evolutionary search, the global search, the stochastic search and the local search could be quickly obtained, in the mean time, the abnormal network behavior patterns with fuzzy detection algorithm could be detected. The benefit of this algorithm is that it does not need the labeled training data sets and it could detect unknown intrusion. Simulation results show that the method mentioned above will be able to detect unknown intrusions with lower false positive rate and higher detection rate.