Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection
基于溯源(provenance)的入侵检测系统(PIDS)通过分析进程、文件、网络等实体之间的系统交互关系来识别高级持续性威胁(APT)。现有方法大多将各类关系(relation,即图中边类型,如读/写文件、网络连接、进程派生等)一视同仁地
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← 返回所有主题基于溯源(provenance)的入侵检测系统(PIDS)通过分析进程、文件、网络等实体之间的系统交互关系来识别高级持续性威胁(APT)。现有方法大多将各类关系(relation,即图中边类型,如读/写文件、网络连接、进程派生等)一视同仁地
该论文关注大规模网络环境下防御高级持续性威胁(APT)的决策理论问题。作者基于近期提出的布尔动力系统(Boolean Dynamical Systems)框架展开研究,该框架能自然刻画 APT 的四个第一性原理要素:攻击的隐蔽性、来自入侵检
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