Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
arXiv:2605.22749v1 Announce Type: new Abstract: Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and control, it also increases vulnerability to cyber-physical disruptions. Operators must distinguish physical incidents, such as faults or line disturbances, from malicious actions, such as false data injection or unauthorized command execution. This chapter investigates this problem using the well-known MSU/ORNL Power System Attack Dataset. The proposed method combines machine learning with geneti
The increasing integration of AI and IoT into critical infrastructure like smart grids creates new vulnerabilities that necessitate advanced anomaly detection methods.
Securing smart grids against cyber-physical attacks is crucial for national security, economic stability, and preventing widespread societal disruption.
The focus on combining machine learning with metaheuristic optimization offers a more robust path to distinguish between genuine physical incidents and malicious cyber-attacks in complex power systems.
- · Cybersecurity firms specializing in critical infrastructure
- · Smart grid operators
- · AI/ML developers for anomaly detection
- · National security agencies
- · Malicious state and non-state actors targeting infrastructure
- · Dated grid security systems
Increased resilience and stability of smart grid operations due to improved threat detection.
Potential for broader adoption of AI-driven cybersecurity solutions across other critical infrastructure sectors.
Deterrence of cyber-physical attacks on energy systems, leading to a reallocation of resources by adversaries towards different vectors.
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Read at arXiv cs.LG