Machine learning based network intrusion detection

Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge as much as the black or white list model. In this paper, we apply the equality constrained-optimization-based extreme learning machine to network intrusion detection. An adaptively incremental learning strategy is proposed to derive the optimal number of hidden neurons. The optimization criteria and a way of adaptively increasing hidden neurons with binary search are developed. The proposed approach is applied to network intrusion detection to examine its capability. Experimental results show our proposed approach is effective in building models with good attack detection rates and fast learning speed.