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| 面向主干网的DNS流量监测 |
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张维维1,2,3, 龚俭1,2,3, 刘尚东1,2,3, 胡晓艳1,2,3
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1.东南大学 计算机科学与工程学院, 江苏 南京 210096;2.江苏省计算机网络重点实验室, 江苏 南京 210096;3.计算机网络和信息集成教育部重点实验室(东南大学), 江苏 南京 210096
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| 摘要: |
| 面对ISP主干网，为了检测威胁其管理域内用户安全的僵尸网络、钓鱼网站以及垃圾邮件等恶意活动，实时监测流经主干网边界的DNS交互报文，并从域名的依赖性和使用位置两个方面刻画DNS活动行为模式，而后，基于有监督的多分类器模型，提出面向ISP主干网的上层DNS活动监测算法DAOS （binary classifier for DNS activityobservation system）.其中，依赖性从用户角度观察域名的外在使用情况，而使用位置则关注区域文件中记录的域名内部资源配置.实验结果表明：该算法在不依赖先验知识的前提下，经过两小时的DNS活动观测，可以达到90.5%的检测准确率，以及2.9%的假阳性和6.6%的假阴性.若持续观察1周，准确率可以上升到93.9%，假阳性和假阴性也可以下降到1.3%和4.8%. |
| 关键词: DNS监测 域名检测 上层DNS流量 DNS活动分析 多分类器 |
| DOI：10.13328/j.cnki.jos.005186 |
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| 基金项目:国家科技支撑计划（2008BAH37B04）；国家基础研究发展计划（973）（2009CB320505）；国家自然科学基金（60973123） |
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| DNS Surveillance on Backbone |
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ZHANG Wei-Wei1,2,3, GONG Jian1,2,3, LIU Shang-Dong1,2,3, HU Xiao-Yan1,2,3
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1.School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;2.Jiangsu Provincial Key Laboratory of Computer Network Technology, Nanjing 210096, China;3.Key Laboratory of Computer Network and Information Integration of Ministry of Education(Southeast University), Nanjing 210096, China
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| Abstract: |
| Focusing on ISP backbone, this paper presents a method to detect malicious activities such as botnets, phishing and spam that threaten user security in the domain by monitoring DNS interaction messages through the network boundary in real time. The method depicts DNS behavior patterns based on dependency and position attribute. Then, the paper proposes a supervised classifier based DNS activity detecting algorithm DAOS (binary classifier for DNS activity observation system). Dependency attribute is used to describe external usage of the domain name from perspective of DNS customer, while position attribute is used to describe resource allocation of records in the zone file. Experimental results show that the algorithm, with a DNS data source in 2 hours, can achieve 90.5% of accuracy, 2.9% of false positive, and 6.6% of false negative without prior knowledge. If the observation is kept for a week, accuracy rises up to 93.9%, false positive and false negative can descend to 1.3% and 4.8%. |
| Key words: DNS surveillance domain name detection upper DNS traffic DNS activity analysis multi-classifier |
