Research on Property and Model Optimization of Multiclass SVM for NIDS

摘要：

　　By investigating insufficiency of typical artificial intelligence algorithms aiming at the high rate of False-Positives and False-Negatives in the Intrusion Detection Systems (IDS),this paper presents an approach that Support Vector Machine(SVM) is embedded in Network Intrusion Detection System (NIDS).At the same time,by using online data and K-fold cross-validation method,this paper proposes a method to optimize the attributes and model of SVM respectively.Experimental results show that by using this method as the detection core of the intrusion detection system,the rate of False-Negatives in IDS can be reduced significantly.

关键词： IDS SVM online detection rate of False-Negatives rate of False-Positives

作者: Jianhao Song Gang Zhao Junyi Song

作者单位: School of Information Management, Beijing Information Science & Technology University Beijing, China

会议类型: 国际会议

会议名称: 2013 2nd International Symposium on Computer,Communication,Control and Automation(ISCCCA-13)(2013年第二届计算机、通信与自动化国际会议)

会议地点: 太原

会议语种:英文

页码: 616-619

在线出版日期: 2013-04-06（万方平台首次上网日期，不代表论文的发表时间）