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| 网络入侵检测系统自体集检测中的概率匹配高效寻优机制 | |
| 引用本文： | 高苗粉,秦勇,李勇,邹裕,李清霞,申林.网络入侵检测系统自体集检测中的概率匹配高效寻优机制[J].计算机应用,2013,33(1):156-159. |
| 作者姓名： | 高苗粉 秦勇 李勇 邹裕 李清霞 申林 |
| 作者单位： | 1. 东莞理工学院 计算机学院，广东 东莞 523808 2. 江苏科技大学 计算机科学与工程学院，江苏 镇江 212003 3. 东莞理工学院城市学院 计算机与信息科学系，广东 东莞 523106 4. 太原理工大学 信息工程学院， 太原 030024 |
| 基金项目： | 广东省教育部产学研项目(2009B090300350);东莞市科技计划项目(2011108102015) |
| 摘 要： | 针对自体集数据规模较大造成的时空上的巨大消耗而难以处理的问题，设计了基于人工免疫的网络入侵检测系统(NIDS)的自体集匹配机制。为提高入侵检测系统的检测效率，提出概率匹配高效寻优机制。首先证明了网络数据的相对集中性，通过计算平均查找长度(ASL)分析了概率匹配机制的有效性，并通过模拟实验验证了该机制的快速匹配效率，并且在一种基于自体集规模简约机制的新型人工免疫网络入侵检测系统上进行了工程应用，取得了较好的匹配效果。 |
| 关 键 词： | 自体集 人工免疫 入侵检测 概率匹配 寻优 |
| 收稿时间： | 2012-07-04 |
| 修稿时间： | 2012-08-09 |
Probability matching efficient-optimization mechanism on self-set detection in network intrusion detection system |
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| GAO Miaofen,QIN Yong,LI Yong,ZOU Yu,LI Qingxia,SHEN Lin.Probability matching efficient-optimization mechanism on self-set detection in network intrusion detection system[J].journal of Computer Applications,2013,33(1):156-159. | |
| Authors: | GAO Miaofen QIN Yong LI Yong ZOU Yu LI Qingxia SHEN Lin |
| Affiliation: | 1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China 2. School of Computer Science, Dongguan University of Technology, Dongguan Guangdong 523808, China 3. Department of Computer and Information Science, City College of Dongguan University of Technology, Dongguan Guangdong 523106, China 4. College of Information Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030024, China |
| Abstract: | To deal with the huge spatial and temporal consumption caused by large-scale self-set data, the authors designed a self-set matching mechanism based on artificial immune Network Intrusion Detection System (NIDS). To improve the detection efficiency of the intrusion detection system, an efficient probability matching optimization mechanism was proposed. The authors first proved the relative concentration of the network data, and analyzed the validity of the probability matching mechanism by calculating the Average Search Length (ASL), then verified the fast matching efficiency of the mechanism through simulation experiments. The mechanism has been used in a project application in a new artificial immune network intrusion detection system based on self-set scale simplified mechanism, which has achieved satisfactory matching results. |
| Keywords: | self-set artificial immune intrusion detection probability matching optimization |
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