An efficient intrusion detection system based on support vector machines and gradually feature removal method

作者：Li, Yinhui; Xia, Jingbo^*; Zhang, Silan; Yan, Jiakai; Ai, Xiaochuan; Dai, Kuobin

来源：Expert Systems with Applications, 2012, 39(1): 424-430.

DOI：10.1016/j.eswa.2011.07.032

• Intrusion detection
• Support vector machine
• Feature reduction

The efficiency of the intrusion detection is mainly depended on the dimension of data features. By using the gradually feature removal method, 19 critical features are chosen to represent for the various network visit. With the combination of clustering method, ant colony algorithm and support vector machine (SVM), an efficient and reliable classifier is developed to judge a network visit to be normal or not. Moreover, the accuracy achieves 98.6249% in 10-fold cross validation and the average Matthews correlation coefficient (MCC) achieves 0.861161.

• 出版日期2012-1
• 单位华中农业大学; 黄冈师范学院