| 摘要: |
| 蠕虫通过发送网络服务请求搜寻感染目标,主机的异常网络服务请求可以作为蠕虫检测的依据.提出了一种蠕虫检测系统,基于阳性选择算法构造自体字符串集合描述主机的正常网络行为.自体字符串集合采用Bloom filter过滤器的形式表示,用于监视主机的网络行为以发现网络中可疑的网络服务请求.依据蠕虫的传播特征,采用二叉树的形式对所发现的可疑网络服务请求进行关联分析,通过无参CUSUM(cumulative sum)算法监视二叉树异常值的变化,从而及时、准确地发现蠕虫传播.GTNetS(Georgia Tech Network Simulation)平台的测试实验结果表明,所提出的蠕虫检测系统能够有效检测蠕虫,同时对于主机正常网络通信的影响较小. |
| 关键词: 蠕虫 人工免疫系统 阳性选择 二叉树 网络仿真 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National High-Tech Research and Development Plan of China under Grant No.2004AA147070 (国家高技术研究发展计划(863)) |
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| Worm Detection System Based on Positive Selection |
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HONG Zheng,WU Li-Fa
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| Abstract: |
| Worms search for targets by means of service requests, and anomalous service requests give indication of worm propagation. A worm detection system that uses positive selection algorithm to characterize normal service requests with self-strings is proposed. Bloom filters are used to represent hosts’ self-strings and monitor the network for suspicious service requests. On the basis of worm properties, the discovered suspicious service requests are correlated in the form of binary trees, and a non-parametric CUSUM (cumulative sum) algorithm is used to monitor the anomaly value of binary trees so as to detect worm propagation timely and accurately. Experimental results of the GTNetS (Georgia Tech Network Simulation) platform show that the proposed system is effective to detect worms, and the system’s influence on normal network traffic is minor. |
| Key words: worm artificial immune system positive selection binary tree network simulation |
