Detecting mass-mailing worm infected hosts by mining DNS traffic data

K. Ishibashi, Tsuyoshi Toyono, Katsuyasu Toyama, Masahiro Ishino, Haruhiko Ohshima, I. Mizukoshi
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引用次数: 49

Abstract

The Domain Name System (DNS) is a critical infrastructure in the Internet; thus, monitoring its traffic, and protecting DNS from malicious activities are important for security in cyberspace. However, it is often difficult to determine whether a DNS query is caused by malicious or normal activity, because information available in DNS traffic is limited.We focus on the activities of mass-mailing worms and propose a method to detect hosts infected by mass-mailing worms by mining DNS traffic data. Our method begins with a small amount of a priori knowledge about a signature query. By assuming that queries sent by most hosts that have sent the signature query of worms have been sent by worm behavior, we detect infected hosts using Bayesian estimation.We apply our method to DNS traffic data captured at one of the largest commercial Internet Service Providers in Japan, and the experimental result indicates that an 89% reduction of mail exchange queries can be achieved with the method.
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通过挖掘DNS流量数据检测群发邮件蠕虫感染主机
域名系统(DNS)是互联网的关键基础设施;因此,监控其流量和保护DNS免受恶意活动对网络空间的安全非常重要。但是,通常很难确定DNS查询是由恶意活动还是正常活动引起的,因为DNS流量中的可用信息是有限的。针对群发邮件蠕虫的活动,提出了一种通过挖掘DNS流量数据来检测群发邮件蠕虫感染主机的方法。我们的方法从签名查询的少量先验知识开始。通过假设大多数发送蠕虫签名查询的主机发送的查询都是由蠕虫行为发送的,我们使用贝叶斯估计检测受感染的主机。我们将该方法应用于日本最大的商业互联网服务提供商之一捕获的DNS流量数据,实验结果表明,使用该方法可以减少89%的邮件交换查询。
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