计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 516-522.doi: 10.11896/jsjkx.210500072
高文龙, 周天阳, 朱俊虎, 赵子恒
GAO Wen-long, ZHOU Tian-yang, ZHU Jun-hu, ZHAO Zi-heng
摘要: 在渗透测试领域,进行攻击路径发现对实现攻击自动化具有重要意义。现有的攻击路径发现算法大多适用于静态全局环境,且存在因状态空间爆炸导致求解失败的问题。为解决动态网络环境下的攻击路径发现问题,提高路径发现效率,提出了基于双向蚁群算法的网络攻击路径发现方法(Attack Path Discovery-Bidirectional Ant Colony Algorithm,APD-BACO)。首先,对网络信息进行建模表示,定义攻击代价;然后,提出一种新的双向蚁群算法进行攻击路径发现,主要的改进包括不同的搜索策略、交叉优化操作和新的信息素更新方式等,仿真实验验证了改进的质量和效率,同时与其他路径发现方法进行对比,结果表明所提方法在较大网络规模下具有一定的时间或空间优势。在攻击路径主机发生故障时,采用重规划机制实现局部区域的攻击路径发现,更适合实际自动化渗透测试下的攻击路径发现。
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