A novel intrusion detection system based on feature generation with visualization strategy

In this paper, a four-angle-star based visualized feature generation approach, FASVFG, is proposed to evaluate the distance between samples in a 5-class classification problem. Based on the four angle star image, numerical features are generated for network visit data from KDDcup99, and an efficient intrusion detection system with less features is proposed. The FASVFG-based classifier achieves a high generalization accuracy of 94.3555% in validation experiment, and the average Mathews correlation coefficient reaches 0.8858.