An intrusion detection algorithm based on multi-label learning

Abstract

Aiming at some problems in current techniques of intrusion detection, this paper puts forward an intrusion detection algorithm based on multi-label k -Nearest Neighbor with multi-label and semi-supervised learning applied. For each unlabeled data, its k nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring data, namely the number of neighboring data belonging to each possible class, MAP (maximum a posteriori) principle is utilized to determine the label set for the unlabeled data. KDD CUP99 data set is implemented to evaluate the proposed algorithm. Compared to other algorithms, the simulation results show that the performance of intrusion detection system is improved.