Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks
arXiv:2605.20391v1 Announce Type: cross Abstract: 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
The proliferation of complex, interconnected systems demands more sophisticated anomaly detection methods that can predict disruptions before they manifest as critical failures.
This research introduces a novel approach to anomaly detection by re-framing security threats as structural deformations within a 'latent geometry,' allowing for earlier intervention and more proactive system resilience.
Anomaly detection shifts from reactive threshold monitoring to proactive structural analysis, enabling the identification of 'pressure' before an actual breach or failure, potentially increasing the security and stability of large-scale networks.
- · Cybersecurity firms
- · Network security departments
- · Intelligence agencies
- · Researchers in network science
- · Adversarial actors exploiting network vulnerabilities
- · Organizations relying solely on traditional anomaly detection
Improved early warning systems for network anomalies, leading to enhanced security for critical infrastructures.
Development of new security products and services based on geometric anomaly detection, creating a new market segment.
The concept of 'latent geometry' as a structural monitor could extend beyond networks to other complex systems, such as financial markets or social dynamics.
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Read at arXiv cs.LG