ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systems
arXiv:2605.25135v1 Announce Type: new Abstract: Anomaly detection in Industrial Internet of Things (IIoT) environments is essential to protect the Industrial Control Systems (ICS) and Cyber-Physical Systems (CPS) from occuring run time false data injection and other malicious attacks. The increasing complexity of sensor networks and interconnected control loops makes it difficult to identify anomalous behavior hidden within high-dimensional and time-dependent signals. To address these challenges, this article introduces Adaptive Spatio-Temporal Reinforcement Optimization ASTRO (ASTRO), a novel
The increasing complexity of IIoT and CPS environments necessitates advanced anomaly detection methods as cyber threats evolve and become more sophisticated.
Securing critical infrastructure against sophisticated cyber attacks is paramount for national security, economic stability, and public safety.
This research introduces a novel reinforcement learning approach for anomaly detection in cyber-physical systems, offering potential for more robust and adaptive security solutions.
- · Industrial control system operators
- · Cybersecurity solution providers
- · IIoT device manufacturers
- · Cyber threat actors
- · Organizations with legacy security systems
Improved defense against false data injection and malicious attacks in industrial environments.
Reduced operational downtime and financial losses due to cyber incidents in critical infrastructure.
Potential for autonomous, self-healing cyber-physical systems that can adapt to novel threats without human intervention.
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