Unsupervised Intrusion Feature Selection based on Genetic Algorithm and FCM

A novel feature selection based on genetic algorithm (GA) and fuzzy c-means clustering (FCM) for unsupervised intrusion detection is proposed. In the method, GA is adopted as search strategy, and the FCM is used to classify the feature data, whose evaluation target is the ratio of the between-class scatter to the within-class scatter. Then, the optimal feature subset and parameters of clustering are found and applied to unsupervised intrusion detection. The experimental results show that the method can solve the feature selection problem and optimization of algorithm parameters in intrusion detection effectively, and it has a better detecting effect than traditional unsupervised intrusion detection.