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Worm Analysis and Changes in Host Behaviors During Worm Outbreaks

机译:蠕虫爆发期间的蠕虫分析和宿主行为的变化

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NetFlow analysis can provide insight into worm behaviors. In this paper, we report the results of a detailed analysis of a publicly available dataset, the 2003 / 2004 wireless traces from the CRAWDAD repository at Dartmouth. This dataset contains packet headers captured from the Dartmouth wireless network between November 2003 and February 2004, a period in which numerous worms were being observed in the wild. IP packet headers were converted to ȁC;degenerateȁD; NetFlow records (1 flow record per packet) and the resulting flow files analyzed using the SiLKtools. Our approach was to use information available on worm and virus outbreaks during the period covered by the data and to use this information to develop profiles for each worm that could be used detect the presence of the wormin the traffic. The initial analysis involved manually studying the traffic for some key ports exploited by worms and looking for changes in network traffic which would give us time windows to filter the traffic for more detailed analysis. In addition to identifying many of the worms active during the period, we observed a number of related phenomena. The initial analysis showed heavy ICMP traffic from early November till second week of January after which it suddenly drops. We also observed sudden appearance of peaks in the SMTP traffic (possibly the result of Mass mailing worms, but more likely due to other causes) throughout the Dataset. In addition to reporting our findings for this data set, we hope that the description of our techniques will serve as a guide for others undertaking similar analyses. We note that the anonymization of the IP addresses and the requirement that we not attempt to break it resulted in our inability to investigate some behaviours in the traffic completely.
机译:NetFlow分析可以洞察蠕虫的行为。在本文中,我们报告了对公开数据集的详细分析结果,该数据集来自达特茅斯(Dartmouth)的CRAWDAD存储库中的2003/2004无线跟踪。该数据集包含2003年11月至2004年2月之间从达特茅斯无线网络捕获的数据包标头,在此期间,在野外观察到许多蠕虫。 IP数据包头被转换为ȁC;简并ȁD; NetFlow记录(每个数据包1个流记录)以及使用SiLKtools分析的结果流文件。我们的方法是在数据覆盖的期间内使用有关蠕虫和病毒爆发的可用信息,并使用此信息为每个蠕虫建立配置文件,以用于检测流量中蠕虫的存在。最初的分析包括手动研究蠕虫利用的一些关键端口的流量,并寻找网络流量的变化,这将使我们有时间窗口来过滤流量以进行更详细的分析。除了确定在此期间活跃的许多蠕虫外,我们还观察到许多相关现象。初步分析显示,从11月初到1月第二周,ICMP流量很大,此后流量突然下降。我们还观察到整个数据集中SMTP流量突然出现峰值(可能是群发邮件蠕虫的结果,但更可能是由于其他原因引起的)。除了报告我们对该数据集的发现之外,我们希望对我们技术的描述将为其他进行类似分析的人提供指导。我们注意到IP地址的匿名化以及我们不试图破坏它的要求导致我们无法完全调查流量中的某些行为。

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