用于企业目录访问图中检测恶意活动的共识-贝叶斯框架
A Consensus-Bayesian Framework for Detecting Malicious Activity in Enterprise Directory Access Graphs
AI总结：
本文提出一种基于共识的贝叶斯框架，用于检测企业目录访问图中的恶意行为，通过动态建模和贝叶斯异常评分机制实现鲁棒检测。
摘要：
本文提出了一种基于共识的贝叶斯框架，用于检测企业目录访问图中的恶意用户行为。通过将目录建模为话题，用户建模为多级交互图中的智能体，我们利用影响加权的意见动力学模拟访问演变。用户之间的逻辑依赖性编码在动态矩阵Ci中，目录相似性通过共享影响矩阵W捕获。恶意行为被注入为跨组件的逻辑扰动，这些扰动违反强连通分量(SCCs)的结构规范。我们应用意见动力学文献中的理论保证来确定主题收敛并检测异常通过缩放的意见方差。为量化不确定性，我们引入了一种贝叶斯异常评分机制，该机制随时间演变，使用静态和在线先验。在合成访问图上的模拟验证了我们的方法，证明了其对逻辑不一致性的敏感性和在动态扰动下的鲁棒性。
英文摘要：
This work presents a consensus-based Bayesian framework to detect malicious user behavior in enterprise directory access graphs. By modeling directories as topics and users as agents within a multi-level interaction graph, we simulate access evolution using influence-weighted opinion dynamics. Logical dependencies between users are encoded in dynamic matrices Ci, and directory similarity is captured via a shared influence matrix W. Malicious behavior is injected as cross-component logical perturbations that violate structural norms of strongly connected components(SCCs). We apply theoretical guarantees from opinion dynamics literature to determine topic convergence and detect anomaly via scaled opinion variance. To quantify uncertainty, we introduce a Bayesian anomaly scoring mechanism that evolves over time, using both static and online priors. Simulations over synthetic access graphs validate our method, demonstrating its sensitivity to logical inconsistencies and robustness under dynamic perturbation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
