Intrusion detection method based on information gain and ReliefF feature selection

Traditional random forest has slow convergence in network intrusion detection and its learning performance is not perfect. In order to eliminate the redundant information in the original intrusion detection data, this paper proposes a random forest intrusion detection method based on the combination of information gain and ReliefF algorithm. The proposed method first uses the information gain to calculate the information gain value of each feature. Then, the ReliefF algorithm is used to calculate the weight of each feature. According to the information gain and the feature weight, the final feature subset is obtained. Finally, this paper uses a random forest classifier for classification. The experiment compares the three feature selection methods, including the proposed method, information gain based method and ReliefF-based method. The experimental results show that the precision, recall rate, and false positive rate of the proposed method are superior to those of the other two methods.