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Towards probabilistic identification of zero-day attack paths

机译：走向零日攻击路径的概率识别

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摘要

Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.
机译：零时差攻击继续挑战企业网络安全防御。当多步骤攻击包含一个或多个零时差攻击时，便形成零时差攻击路径。及时发现零日攻击路径可以使早期发现零日威胁成为可能。在本文中，我们提出了一种概率方法来识别零日攻击路径并实现一个名为ZePro的原型系统。首先从系统调用中构建对象实例图，以捕获入侵传播。为了进一步揭示隐藏在实例图中的零日攻击路径，我们的系统构建了一个基于实例图的贝叶斯网络。通过利用入侵证据，贝叶斯网络可以定量计算被感染对象实例的概率。具有高感染概率的对象实例会自我展示，并形成零日攻击路径。实验结果表明，我们的系统可以有效地识别零日攻击路径。

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