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    叶 云, 徐锡山, 齐治昌, 吴雪阳. 大规模网络中攻击图自动构建算法研究[J]. 计算机研究与发展, 2013, 50(10): 2133-2139.
    引用本文: 叶 云, 徐锡山, 齐治昌, 吴雪阳. 大规模网络中攻击图自动构建算法研究[J]. 计算机研究与发展, 2013, 50(10): 2133-2139.
    Ye Yun, Xu Xishan, Qi Zhichang, Wu Xueyang. Attack Graph Generation Algorithm for Large-Scale Network System[J]. Journal of Computer Research and Development, 2013, 50(10): 2133-2139.
    Citation: Ye Yun, Xu Xishan, Qi Zhichang, Wu Xueyang. Attack Graph Generation Algorithm for Large-Scale Network System[J]. Journal of Computer Research and Development, 2013, 50(10): 2133-2139.

    大规模网络中攻击图自动构建算法研究

    Attack Graph Generation Algorithm for Large-Scale Network System

    • 摘要: 随着计算机技术和网络通信技术的飞速发展, 网络安全形势日趋严峻.攻击者往往采取多步骤网络攻击的方式对网内多个漏洞实施逐步击破,而攻击图正好刻画了目标网络内潜在威胁的传播路径.针对目前攻击图构建算法无法很好地适用于大规模目标网络的问题,通过深入分析传统攻击图构建算法的不足和目标环境的特点,提出了一种新的构建攻击图的方法.首先,采用攻击图建模语言(Attack Graphs Modeling Language, AGML)形式化描述漏洞知识库和目标环境;其次,提出了目标环境的预处理技术,为目标环境中的属性建立索引,然后利用攻击模式的实例化技术构建攻击图.通过对该算法的时间复杂度分析和模拟实验验证,表明该算法具有良好的可扩展性,能够为具有复杂网络拓扑结构的大规模目标网络自动构建攻击图.

       

      Abstract: At present, with the rapid development of computer technology and network communication technology, the network security becomes more and more serious. An attacker can often infiltrate a seemingly well-guarded network system to promulgate threats using multi-step attacks by exploiting sequences of related vulnerabilities. And fortunately, the attack graphs are able to reveal such potential threats by enumerating all possible sequences of atomic attacks. Aiming at the problems that it is difficult to generate attack graphs for large network system, a scalable approach is proposed to generate the full attack graphs based on the in-depth analysis of the models' features of the network environment and the limitation of previous algorithms. Firstly, a novel modeling language AGML (Attack Graphs Modeling Language) is proposed, which describes the attack patterns and initial scenario. Secondly, a scalable approach is put forward to generate full attack graphs through the technologies of creating index for the attributes and instantiating attack patterns. Furthermore, the algorithm has been tested on simulated networks. The experimental result shows the approach could be applied to large-scale networks.

       

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