潜在几何作为结构监视器：用于匿名网络异常检测的特征空间对齐
Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks
AI总结：
本文提出利用潜在几何结构来监测匿名网络中的异常，通过特征空间对齐方法检测行为群体中的异常模式，展示了在Tor网络中通过双观察者流程识别稳定九维负载子空间的方法，并验证了其结构稳定性。
摘要：
传统异常检测在测量信号超过预设阈值时标记事件，这捕捉到了转变的时刻，但未能捕捉到其前的结构性压力。我们提出将大规模行为群体视为几何能量景观，其变形可以在主要转变前和期间测量。核心论点是结构优先于几何：行为群体的结构组织是信号，而几何度量是测量它的工具。应用于Tor匿名网络连续67天的观测窗口，双观察者流程识别出一个在观测期间保持不变的九维负载子空间，并通过蒙特卡洛模拟在噪声底面以上16.8西格玛验证了该结构。主要检测门在24个确认稳定的窗口中实现了0.0%的误报率。对2026年2月20日确认的基础设施事件的调查正式否定了中继退出假说，识别出连接降级而无拓扑变化为可检测的网络故障模式。结果是一种候选的结构监视框架，适用于具有足够遥测数据的行为群体。
英文摘要：
Traditional anomaly detection marks events when measured signals cross predefined thresholds. This captures the moment of transition but not the structural pressure that precedes it. We propose treating large behavioral populations as geometric energy landscapes whose deformation can be measured before and during major transitions. The central thesis is that structure precedes geometry: the structural organization of the population is the signal, and geometric metrics are instruments for measuring it. Applied to the Tor anonymity network across 67 consecutive daily observation windows, the dual-observer pipeline identifies a stable nine-dimensional load-bearing subspace invariant across the observation period and validates this structure by Monte Carlo simulation at 16.8 sigma above the noise floor. Primary detection gates achieve 0.0% false positive rate on 24 confirmed stable windows. Forensic analysis of the February 20, 2026 confirmed infrastructure event formally falsifies the relay-departure hypothesis, identifying connectivity degradation without topology change as a detectable network failure mode. The result is a candidate structural-monitoring framework for behavioral populations with sufficient telemetry.
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。
