Heat-ray:使用机器学习、组合优化和攻击图来对抗身份雪球攻击

John Dunagan, A. Zheng, Daniel R. Simon
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引用次数: 33

摘要

随着计算机的互联程度越来越高,安全配置的复杂性也呈爆炸式增长。管理工具没有跟上步伐,我们表明,这已经使身份雪球攻击成为一种严重的危险。身份雪球攻击利用登录到第一个受损主机的用户,利用这些用户的特权在其他主机上发起额外的攻击。为了打击此类攻击,我们提出了Heat-ray,这是一个结合了机器学习、组合优化和攻击图的系统,可扩展地管理安全配置。通过对一个拥有数十万用户和机器的组织的评估,我们表明Heat-ray可以使IT管理员减少96%的机器数量,这些机器可以用来发动大规模的身份滚雪球攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heat-ray: combating identity snowball attacks using machinelearning, combinatorial optimization and attack graphs
As computers have become ever more interconnected, the complexity of security configuration has exploded. Management tools have not kept pace, and we show that this has made identity snowball attacks into a critical danger. Identity snowball attacks leverage the users logged in to a first compromised host to launch additional attacks with those users' privileges on other hosts. To combat such attacks, we present Heat-ray, a system that combines machine learning, combinatorial optimization and attack graphs to scalably manage security configuration. Through evaluation on an organization with several hundred thousand users and machines, we show that Heat-ray allows IT administrators to reduce by 96% the number of machines that can be used to launch a large-scale identity snowball attack.
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