Network Intrusion Detection by Support Vectors and Ant Colony

摘要：

This paper presents a framework for a new approach in intrusion detection by combining two existing machine learning methods (i.e. SVM and CSOACN). The IDS based on the new algorithm can be applied as pure SVM, pure CSOACN or their combination by constructing the detection classifier under three different training modes respectively. The initial experiments indicate that performance of their combination is better than pure SVM in terms of higher average detection rate as well as lower rates of both negative and positive false and is better than pure CSOACN in terms of less training time with comparable detection rate and false alarm rates.

关键词： Network security network attack Intrusion Detection Systems (IDS) Support Vector Machine (SVM) Ant Colony Network

作者: Qinglei Zhang Wenying Feng

作者单位: Department of Computing and Software, Faculty of Engineering, McMaster University, Hamilton, ON, Can Departments of Computing & Information Systems Department of Mathematics Trent University, Peterboro

会议类型: 国际会议

会议名称: 2009 International Workshop on Information Security and Application(2009 信息安全与应用国际研讨会)

会议地点: 青岛

会议语种:英文

页码: 639-642

在线出版日期: 2009-11-21（万方平台首次上网日期，不代表论文的发表时间）