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Detecting smart, self-propagating Internet worms

机译：检测智能，自宣传互联网蠕虫

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Self-propagating worms can infect millions of computers on the Internet in just several minutes. Although there are already many existing worm detectors, none of them systematically consider the countermeasures from worm authors, leaving them potentially ineffective against smart, evasive worms. We therefore revisit worm detection in this paper. We treat worm detection as an arms race, and study how to most effectively detect not only classic worms (i.e. worms that do not have the knowledge of worm detectors), but also evasive worms that know the worm detector in place, know its configurations, and can even adjust their scanning rate by observing legitimate traffic. We describe our design of a new worm detector called SWORD, conduct extensive experiments using realistic trace with different parameters of worms, and demonstrate that SWORD is superior to existing detectors for detecting both classic and evasive worms.
机译：自我传播的蠕虫可以在几分钟内在互联网上感染数百万台电脑。 虽然已经存在许多现有的蠕虫探测器，但它们都没有系统地考虑来自蠕虫作者的对策，让他们对智能，避免蠕虫潜在地无效。 因此，我们在本文中重新审视了蠕虫检测。 我们将蠕虫检测视为武器种族，研究如何最有效地检测经典蠕虫（即没有蠕虫探测器知识的蠕虫），而且还知道知道蠕虫探测器的蠕虫，了解其配置， 并且甚至可以通过观察合法的交通来调整扫描率。 我们描述了我们设计的新蠕虫探测器，称为剑，使用具有不同蠕虫的参数的现实轨迹进行广泛的实验，并证明剑优于现有的探测器，用于检测经典和避免的蠕虫。

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