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An efficient intrusion detection system based on support vector machines and gradually feature removal method |
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| Authors: | Yinhui LiJingbo Xia Silan ZhangJiakai Yan Xiaochuan AiKuobin Dai |
| Affiliation: | a College of Science, Huazhong Agricultural University, Wuhan, Hubei, China b College of Science, Navy Engineering University, Wuhan, Hubei, China c College of Math and Info Science, Huanggang Normal University, Huanggang, Hubei, China |
| Abstract: | 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. |
| Keywords: | Intrusion detection Support vector machine Feature reduction |
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