|
|||||
|
|
| 粒子群优化的半监督入侵检测算法 | |
| 引用本文： | 贾志伟,关忠仁,赵建芳.粒子群优化的半监督入侵检测算法[J].成都信息工程学院学报,2012,27(3):233-238. |
| 作者姓名： | 贾志伟 关忠仁 赵建芳 |
| 作者单位： | 1. 成都信息工程学院计算机学院,四川成都,610225 2. 成都信息工程学院信息中心,四川成都,610225 3. 江南大学物联网工程学院,江苏无锡,214000 |
| 基金项目： | 四川省教育厅2010年科研资助项目 |
| 摘 要： | 为解决无监督入侵检测算法检测率低,有监督的入侵检测不能有效的检测未知攻击的问题,提出了一种粒子群优化的半监督入侵检测算法,算法对少量的约束信息进行基于密度的扩展获得潜在约束得到聚类模型,以此指导未标记数据聚类,对仍没有确定类别的未标示数据使用粒子群优化的K均值算法进行聚类实现对异常的检测。改进的算法检测率达到83.7%,误报率减少至3.13%,总体效果优于无监督和有监督学习的入侵检测算法。 |
| 关 键 词： | 计算机应用技术 数据挖掘 半监督聚类 入侵检测 约束扩展 粒子群优化算法 |
PSO-based Semi-supervised Intrusion Detection Algorithm |
|
| JIA Zhi-wei , GUAN Zhong-ren , ZHAO Jian-fang.PSO-based Semi-supervised Intrusion Detection Algorithm[J].Journal of Chengdu University of Information Technology,2012,27(3):233-238. | |
| Authors: | JIA Zhi-wei GUAN Zhong-ren ZHAO Jian-fang |
| Institution: | 1.Chengdu university of information technology,college of computer science,Chengdu 610225,China;2.Chengdu university of information technology,information center,Chengdu 610225,China;3.Jiangnan University,IoT Engineering college,Wuxi 214000,China) |
| Abstract: | View of unsupervised intrusion detection algorithm has low detection rate,supervised intrusion detection can not detect unknown attacker,this paper proposed a PSO-based semi-supervised Intrusion detection algorithm,it will expand a small amount of constraint information with density expansion method to obtain the clustering model,which used to guide the unlabeled data clustering,at last the unmarked data which was not determined its category,which using particle swarm optimization k-means algorithm to cluster to achieve the detection of abnormal.Algorithm improved the detection rate to 83.7% and the false positive rate decreased to 3.13%,the overall effect is superior to intrusion detection based on unsupervised and supervised learning. |
| Keywords: | applied computer technology data mining semi-supervised clustering intrusion detection construction detection PSO |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |