Data Availability and Integrity Attacks for Power Systems via Nonlinear Spatial-Temporal Modeling

Abstract

Cybersecurity has become critical for modern power systems as they increasingly rely on digital communication and computational frameworks. Among cyber-attacks, Data Availability and Integrity Attacks (DAIA)—comprising availability attacks such as Denial-of-Service (DoS) and integrity attacks like replay attack—pose severe risks by degrading the reliability and usability of measurement data essential for operational decision-making. This paper presents a data-driven model using the Volterra series to address DAIA in complex power systems. The proposed model effectively captures spatial-temporal relationships, integrating multiple data sources with varying sampling rates while addressing nonlinear dynamics introduced by renewable energy integration. We develop a scalable anomaly detection framework that identifies such attack patterns, including replay attacks leveraging training data. Extensive simulations on the 240-bus WECC system demonstrate the model’s superior performance, achieving robust detection and accurate data recovery with practical computational requirements. Our work provides actionable insights and configuration guidelines for ensuring power system resilience against evolving cyber threats.

Keywords

• Spatial-temporal modeling
• multiple inputs resolution
• data availability and integrity attack (DAIA)