A Two-step Feature Selection Algorithm Adapting to Intrusion Detection

In intrusion detection data set is high dimensional, which leads to low processing speed for intrusion detection algorithms, but it holds many features affecting little for detection. To address the above issue, a two-step feature selection algorithm is proposed in this paper. Depending on the definition of relevant feature and redundant feature and using mutual information as criterion, it firstly eliminates the irrelevant features and then eliminates the redundant features. With low time complexity, the feature selection algorithm independent of detection algorithm could easily balance the detection accuracy and the number of features through threshold. Experiments over networks connection records from authoritative data set KDD CUP 1999 were implemented for several detection algorithms to evaluate the proposed method. The results show the algorithm could effectively select features, ensure detection accuracy and improve processing speed.