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An Artificial Intelligence Based Approach for Risk Management Using Attack Graph

机译:基于攻击图的基于人工智能的风险管理方法

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In today's large complex organizational network,security is a challenging task for most of the administrators.The typical means by which an attacker breaks into a network is through a series of exploits,where each exploit in the series satisfies the pre-condition for subsequent exploits and makes a causal relationship among them.Such a series of exploits constitutes an attack path and the set of all possible attack paths form an attack graph.Present day vulnerability scanners are able to identify the vulnerabilities in isolation but there is a need for correlation among these vulnerabilities to identify overall risk of the network.In this paper we propose a novel approach by finding out an attack path consisting of logically connected exploits and extends it to an attack graph.The solution also finds out the set of root cause vulnerabilities for overall security threat while taking care the inherent time and scalability problem of attack graph generation.
机译:在当今的大型复杂组织网络中,安全性对于大多数管理员而言都是一项艰巨的任务。攻击者入侵网络的典型方法是通过一系列利用,其中一系列利用均满足后续利用的先决条件。这样的一系列攻击构成了一条攻击路径,所有可能的攻击路径的集合形成了一个攻击图。当今的漏洞扫描程序能够隔离识别漏洞,但需要在它们之间建立关联。这些漏洞可以识别网络的整体风险。在本文中,我们提出了一种新颖的方法,即找出由逻辑连接的漏洞构成的攻击路径,并将其扩展到攻击图。该解决方案还找出了导致整个网络的根本原因漏洞的集合。安全威胁,同时要注意攻击图生成的固有时间和可伸缩性问题。

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