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Effectiveness of proactive reset for mitigating impact of stealthy attacks on networks of autonomous systems

机译：主动重置的有效性，以减轻对自动系统网络的隐式攻击的影响

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摘要

Recent examples have shown that sophisticated cyber attackers are capable of infiltrating the cyber defenses of major organizations and spreading stealthily through a network, potentially doing significant damage before exploited vulnerabilities can be identified or patches developed. Autonomous systems are particularly vulnerable because they are further removed from human intervention. One emerging technology designed to address this problem is proactive reset, where systems automatically undergo a reset operation that restores them to a known malware-free state, regardless of whether or not they were already infected. More frequent resets result in higher security, but may also reduce functionality of the network. In this work, we consider the effectiveness of three proactive reset policies for mitigating the spread of stealthy malware through a network of autonomous systems. We perform experiments using agent-based simulation and find that a proactive policy that uses risk-flow analysis to determine when systems should be reset achieves performance comparable to that of a perfect detector.
机译：最近的例子表明，复杂的网络攻击者能够渗透主要组织的网络防御，并通过网络秘密传播，从而可能在识别出被利用的漏洞或开发补丁之前造成重大破坏。自治系统特别容易受到攻击，因为它们已被进一步从人工干预中移除。一种旨在解决此问题的新兴技术是主动重置，其中系统自动进行重置操作，无论它们是否已被感染，都将其还原到已知的无恶意软件状态。更频繁的重置会导致更高的安全性，但也会降低网络的功能。在这项工作中，我们考虑了三种主动重置策略在缓解通过自治系统网络进行的隐式恶意软件传播方面的有效性。我们使用基于代理的模拟进行实验，发现使用风险流分析来确定何时应重置系统的主动策略所获得的性能可与完美检测器相媲美。

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