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Detecting Malicious Flux Service Networks through Passive Analysis of Recursive DNS Traces

机译:通过对递归DNS跟踪进行被动分析检测恶意流量服务网络

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In this paper we propose a novel, passive approach for detecting and tracking malicious flux service networks. Our detection system is based on passive analysis of recursive DNS (RDNS) traffic traces collected from multiple large networks. Contrary to previous work, our approach is not limited to the analysis of suspicious domain names extracted from spam emails or precompiled domain blacklists. Instead, our approach is able to detect malicious flux service networks in-the-wild, i.e., as they are accessed by users who fall victims of malicious content advertised through blog spam, instant messaging spam, social website spam, etc., beside email spam. We experiment with the RDNS traffic passively collected at two large ISP networks. Overall, our sensors monitored more than 2.5 billion DNS queries per day from millions of distinct source IPs for a period of 45 days. Our experimental results show that the proposed approach is able to accurately detect malicious flux service networks. Furthermore, we show how our passive detection and tracking of malicious flux service networks may benefit spam filtering applications.
机译:在本文中,我们提出了一种新颖的被动方法来检测和跟踪恶意流量服务网络。我们的检测系统基于对从多个大型网络收集的递归DNS(RDNS)流量跟踪的被动分析。与以前的工作相反,我们的方法不仅限于分析从垃圾邮件或预先编译的域名黑名单中提取的可疑域名。相反,我们的方法能够在野外检测恶​​意的流量服务网络,即当用户访问通过博客垃圾邮件,即时消息垃圾邮件,社交网站垃圾邮件等广告宣传的恶意内容的受害者时,它们就会被访问。垃圾邮件。我们尝试在两个大型ISP网络上被动收集RDNS流量。总体而言,在45天内,我们的传感器每天监控来自数百万个不同源IP的超过25亿个DNS查询。我们的实验结果表明,该方法能够准确检测恶意流量服务网络。此外,我们展示了被动检测和跟踪恶意流量服务网络如何使垃圾邮件过滤应用程序受益。

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