| Zhang Fengbin（张凤斌）,Xi Liang,Wang Shengwen.Real-valued multi-area self set optimization in immunity-based network intrusion detection system[J].高技术通讯(英文),2012,18(1):1~6 |
| Real-valued multi-area self set optimization in immunity-based network intrusion detection system |
| Real-valued multi-area self set optimization in immunity-based network intrusion detection system |
| Received:April 05, 2012 |
| DOI： |
| 中文关键词: immunity-based, network intrusion detection system (NIDS), real-valued, self set, optimization |
| 英文关键词: immunity-based, network intrusion detection system (NIDS), real-valued, self set, optimization |
| 基金项目: |
| Author Name | Affiliation | | Zhang Fengbin（张凤斌） | | | Xi Liang | | | Wang Shengwen | |
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| 中文摘要: |
| The real-valued self set in immunity-based network intrusion detection system (INIDS) has some defects: multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system’s detection rate. |
| 英文摘要: |
| The real-valued self set in immunity-based network intrusion detection system (INIDS) has some defects: multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system’s detection rate. |
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