Research on network intrusion detection based on incremental extreme learning machine and adaptive principal component analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.