| 一种基于独特型网络的入侵检测方法 |
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| 引用本文： | 赵林惠,戴亚平,付东梅,董芳艳.一种基于独特型网络的入侵检测方法[J].北京理工大学学报,2006,26(9):809-812. |
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| 作者姓名： | 赵林惠 戴亚平 付东梅 董芳艳 |
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| 作者单位： | 北京理工大学,信息科学技术学院自动控制系,北京,100081;东京工业大学,大学院综合理工学研究科,智能系统科学专攻,横滨,226-8502 |
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| 基金项目： | 国家部委预研项目
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北京理工大学校科研和教改项目 |
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| 摘 要： | 为解决神经网络检测方法中检测器需要定期更新、未知攻击检测性能低等问题,利用人工独特型网络的记忆、学习和动态调整能力实现入侵检测.提出一种可用作检测器的多变异模式人工独特型网络,并根据免疫响应原理设计检测算法,使检测器能实时学习新行为特征.仿真结果表明,多变异模式独特型网络检测方法与多层感知器检测方法相比,平均误报率下降了17.43%,未知攻击的平均检测准确率提高了24.17%.
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| 关 键 词： | 入侵检测 免疫网络理论 人工独特型网络 |
| 文章编号： | 1001-0645(2006)09-0809-04 |
| 收稿时间： | 04 13 2006 12:00AM |
| 修稿时间： | 2006年4月13日 |
Intrusion Detection Approach Based on Artificial Idiotypic Network |
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| ZHAO Lin-hui,DAI Ya-ping,FU Dong-mei and DONG Fang-yan.Intrusion Detection Approach Based on Artificial Idiotypic Network[J].Journal of Beijing Institute of Technology(Natural Science Edition),2006,26(9):809-812. |
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| Authors: | ZHAO Lin-hui DAI Ya-ping FU Dong-mei and DONG Fang-yan |
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| Institution: | Department of Automatic Control, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;Department of Automatic Control, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;Department of Automatic Control, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;Department of Computer Intelligence & System Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama 2268502, Japan |
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| Abstract: | To overcome defects existing in methods based on neural networks,such as the periodical update on detectors and poor performance on unknown attacks,the memory,learning and dynamic regulating abilities of artificial idiotypic networks are used to implement intrusion detection approaches.A multi-mutation-pattern artificial idiotypic network is presented to be used as detectors.By utilizing the immune response principle,the detection algorithm is designed.New behavior features are learnt by detectors in real-time.The detection approach based on multi-mutation-pattern artificial idiotypic network is compared with the detection approach based on multilayer perceptrons through simulations.The results show that the average false positive rate is decreased by 17.43% and the average detection accuracy of unknown attacks is increased by 24.27%. |
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| Keywords: | intrusion detection immune networks theory artificial idiotypic networks |
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