A taxonomy and adversarial model for attacks against network log anonymization
A taxonomy and adversarial model for attacks against network log anonymization
复制标题
针对网络日志匿名化攻击的分类和对抗模型
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
A. Slagell
中科院分区：
文献类型：
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作者：
Justin King;Kiran Lakkaraju;A. Slagell
近年来，对于研究人员、安全事件响应者和教育工作者来说，共享网络日志变得非常重要，并且已经提出了许多日志匿名化工具和技术来清理这种敏感的数据源，以便实现更多的协作。不幸的是，许多新的攻击已经被创建，同时，试图利用匿名化过程中的弱点。在本文中，我们提出了一个分类，涉及类似的攻击在一个有意义的方式。我们还提出了一个新的对抗模型，我们可以通过特定对手可能犯下的攻击类型映射到分类中。这有助于我们在数据效用和信任之间进行权衡，为我们提供了一种方法来指定匿名化方案的强度，以衡量它所保护的对手类型。
In recent years, it has become important for researchers, security incident responders and educators to share network logs, and many log anonymization tools and techniques have been put forth to sanitize this sensitive data source in order to enable more collaboration. Unfortunately, many new attacks have been created, in parallel, that try to exploit weaknesses in the anonymization process. In this paper, we present a taxonomy that relates similar kinds of attacks in a meaningful way. We also present a new adversarial model which we can map into the taxonomy by the types of attacks that can be perpetrated by a particular adversary. This has helped us to negotiate the trade-offs between data utility and trust, by giving us a way to specify the strength of an anonymization scheme as a measure of the types of adversaries it protects against.