Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems
arXiv:2607.06094v1 Announce Type: new Abstract: Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model normal behaviour; the standard point-adjusted evaluation, however, rewards detectors that never do. CPS normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes rather than a single blob; we state this structure as ten assumptions (A1-A10), abbreviated Massive, Implicit, Imbalanced Multimodality (MIIM). We model the normal law with a jointly learned latent represen
This research addresses the critical challenge of anomaly detection in complex cyber-physical systems, a field gaining urgency with increasing integration and sophistication of critical infrastructure.
Improving anomaly detection in cyber-physical systems is vital for maintaining stability, preventing disruptions, and ensuring security across industries ranging from defense to critical infrastructure.
The proposed 'joint latent clustering' method offers a more robust approach to modeling normal behavior in systems with massive and imbalanced multimodality, leading to better fault detection.
- · Cyber-Physical System operators
- · AI/ML security firms
- · Critical infrastructure sectors
- · Defense industry
- · Malicious actors targeting CPS
- · Organizations with legacy anomaly detection systems
Enhanced security and reliability of complex integrated systems due to more accurate anomaly detection.
Reduced operational downtime and financial losses from system failures or cyberattacks in critical sectors.
Accelerated adoption of AI-driven security solutions across government and industry, potentially creating new market leaders in cyber resilience.
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Read at arXiv cs.LG