The intrusion detection system deals with huge amount of data containing redundant and noisy features and the poor classifier algorithm causing the degradation of detection accuracy, in this paper, we introduce the random forest feature selection algorithm and propose a method that multi-classifier ensemble based on deep learning for intrusion detection. It used the random forest feature selection algorithm to extract optimal feature subset that are used to train by support vector machine, decision tree, naïve bayes and k-nearest neighbor classification algorithm, then, applying the deep learning to stack the output of four classifiers. The experimental results show that the proposed method can effectively improve the accuracy of intrusion detection compared with the majoring voting algorithm.
Ling et al. REF introduced a random forest feature selection algorithm and proposed a multi-classifier method.
Feature Selection and Deep Learning based Approach for Network Intrusion Detection
Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Engineering