相关噪声下的组合异常检测的主动假设检验
Active Hypothesis Testing for Correlated Combinatorial Anomaly Detection
AI总结：
ECC-AHT通过主动噪声抵消技术，在相关噪声环境下实现高效的组合异常检测
摘要：
我们研究了在相关噪声下识别异常子集流的问题，这受到网络和物理系统监控和安全性的启发。这个问题可以被视为一种组合纯探索形式，其中每个流扮演一个臂的角色，必须在不确定性下按顺序分配测量。现有的组合老虎机和假设检验方法通常假设独立观察，无法利用相关性进行高效的测量设计。我们提出了ECC-AHT，一种自适应算法，通过选择连续、受约束的测量来最大化竞争假设之间的Chernoff信息，通过差异传感实现主动噪声抵消。ECC-AHT实现了最优样本复杂性保证，并在合成和现实中的相关环境中显著优于最先进的基线方法。代码可在https://github.com/VincentdeCristo/ECC-AHT上获得。
英文摘要：
We study the problem of identifying an anomalous subset of streams under correlated noise, motivated by monitoring and security in cyber-physical systems. This problem can be viewed as a form of combinatorial pure exploration, where each stream plays the role of an arm and measurements must be allocated sequentially under uncertainty. Existing combinatorial bandit and hypothesis testing methods typically assume independent observations and fail to exploit correlation for efficient measurement design. We propose ECC-AHT, an adaptive algorithm that selects continuous, constrained measurements to maximize Chernoff information between competing hypotheses, enabling active noise cancellation through differential sensing. ECC-AHT achieves optimal sample complexity guarantees and significantly outperforms state-of-the-art baselines in both synthetic and real-world correlated environments. The code is available on https://github.com/VincentdeCristo/ECC-AHT
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。
