Intrusion detection based on the semi-supervised Fuzzy C-Means clustering algorithm

The intrusion detection algorithm based on the supervised learning has a high detection rate, but all the labeled data which hard to collect are needed when the algorithm used. Meanwhile the intrusion detection algorithm based on the unsupervised learning has a high False Positive Rate. In this paper a semi-supervised learning algorithm for intrusion detection is proposed combined with the Fuzzy C-Means algorithm. The sensitivity to the initial values and the probability of trapping in local optimum are greatly reduced by using few labeled data to improve the learning ability of the FCM algorithm. The KDD CUP99 data set is adopted as the experimental subject. The result proves that the attack behaviors can be more efficiently found from the network data by the semi-supervised FCM clustering algorithm.