A novel intrusion detection method based on clonal selection clustering algorithm

This paper presents a novel unsupervised fuzzy clustering method based on clonal selection algorithm for anomaly detection in order to solve the problem of fuzzy k-means algorithm which is much more sensitive to the initialization and easy to fall into local optimization. This method can quickly obtain the global optimal clustering with clonal operator which combines the evolutionary search, global search, stochastic search and local search. And then detect abnormal network behavioral patterns with fuzzy detection algorithm. Experimental results on the data set of KDD99 show that this method can detect unknown intrusions with lower time complexity and higher detection rate.