| Multimodal Evolution Approach to Multidimensional Intrusion Detection |
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| 引用本文： | 翁广安 余胜生 周敬利. Multimodal Evolution Approach to Multidimensional Intrusion Detection[J]. 西南交通大学学报(英文版), 2006, 14(3): 212-217 |
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| 作者姓名： | 翁广安 余胜生 周敬利 |
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| 作者单位： | Computer Institute, Huazhong University of Science and Technology, Wuhan 430074, China |
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| 摘 要： | An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets （machine learning database and DAPRA99） indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stabih＇ty, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.
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| 关 键 词： | 多峰演化 检测器 计算方法 侵入检测 |
| 文章编号： | 1005-2429（2006）03-0212-06 |
| 收稿时间： | 2005-08-10 |
Multimodal Evolution Approach to Multidimensional Intrusion Detection |
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| Weng Guang'an,Yu Shengsheng,Zhou Jingli. Multimodal Evolution Approach to Multidimensional Intrusion Detection[J]. Journal of Southwest Jiaotong University, 2006, 14(3): 212-217 |
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| Authors: | Weng Guang'an Yu Shengsheng Zhou Jingli |
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| Abstract: | An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets (machine learning database and DAPRA99) indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stability, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones. |
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| Keywords: | Artificial immune systems Intrusion detection Multimodal evolution Hyper-sphere Hyper-rectangle |
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