| 基于多智能体的自动免疫接种模型 |
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| 引用本文： | 郑凯元,叶茂,李筝,朱莺嘤.基于多智能体的自动免疫接种模型[J].计算机工程与应用,2008,44(8):148-151. |
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| 作者姓名： | 郑凯元 叶茂 李筝 朱莺嘤 |
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| 作者单位： | 1.电子科技大学 计算机学院，成都 610054 2.电子科技大学 通信学院，成都 610054 |
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| 基金项目： | 四川省科技厅应用基础研究基金( the Fundamental and Application Research Foundation of Science and Technology Bureau of SichuanProvince under Grant No.2006J13～065)，教育部新世纪优秀人才支持计划( the Program for New Century Excellent Talents in Universityunder Grant No.NCET- 06- 0811) |
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| 摘 要： | 随着互联网的发展，网络风险随处可见，联网计算机迫切需要一种及时有效的方法来检测并避免网络风险。为了解决全网对网络入侵的快速检测和快速免疫问题，文章结合人工免疫思想和并行遗传算法提出了一种能够针对网络入侵，自动提取入侵疫苗，并对全网进行免疫接种的模型。模型中采用并行遗传算法提取疫苗，融合各个种群的优良个体得到最佳疫苗，通过基于关键主机的免疫信息发布算法对主机进行免疫接种，从而达到全网的免疫效果。
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| 关 键 词： | 人工免疫 入侵检测 多智能体 网络免疫接种 |
| 文章编号： | 1002-8331（2008）08-0148-04 |
| 收稿时间： | 2007-07-09 |
| 修稿时间： | 2007-09-14 |
Novel automatic immunization model based on immunology and multi-Agent |
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| ZHENG Kai-yuan,YE Mao,LI Zheng,ZHU Ying-ying.Novel automatic immunization model based on immunology and multi-Agent[J].Computer Engineering and Applications,2008,44(8):148-151. |
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| Authors: | ZHENG Kai-yuan YE Mao LI Zheng ZHU Ying-ying |
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| Affiliation: | 1.School of Computer Science and Engineering，University of Electronic Science and Technology of China，Chengdu 610054，China 2.School of Comm. and Information Engineering，University of Electronic Science and Technology of China，Chengdu 610054，China |
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| Abstract: | With the development of the Internet,network intrusions are all around us.The key to the intrusion detection is that how to discover the new threat in time and make the computer immunized.To solve this problem,a novel automatic immunization model,which can form the vaccine of network virus automatically and immunize computer nodes in the whole network,has been brought out.In this model,the optimal vaccine will be obtained by the way of parallel genetic algorithm.After that,by using the network distribution algorithm,in order to keep the whole network running normally,the key node of the network will be immunized at first. |
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| Keywords: | artificial immunity intrusion detection multi-Agent network immunization |
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