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On Pseudonymization of Audit Data for Intrusion Detection

机译:关于入侵检测审计数据的假义

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In multilaterally secure intrusion detection systems (IDS) anonymity and accountability are potentially conflicting requirements. Since IDS rely on audit data to detect violations of security policy, we can balance above requirements by pseudonymization of audit data, as a form of reversible anonymization. We discuss previous work in this area and underlying trust models. Instead of relying on mechanisms external to the system, or under the control of potential adversaries, in our proposal we technically bind reidentification to a threshold, representing the legal purpose of accountability in the presence of policy violations. Also, we contrast our notion of threshold-based identity recovery with previous approaches and point out open problems.
机译:在多边保护的入侵检测系统(IDS)中,匿名和问责制具有潜在冲突的要求。由于ID依赖于审计数据来检测违反安全策略,因此我们可以通过审计数据的假义数据来平衡以上要求,作为可逆匿名化的形式。我们讨论此领域的以前的工作和基础的信任模型。在我们的建议中,在我们的建议中,我们在技术上将责任违反责任的法律目的,在我们的建议下,而不是依赖于系统外部的机制,而是根据系统的控制。此外,我们将基于阈值的身份恢复的概念与先前的方法进行了鲜明对比,并指出打开问题。

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