<ul><li>This study focuses on anomaly detection in cyber-physical systems (CPS) using an efficient and sustainable approach.</li><li>The proposed hybrid TDC-AE approach captures the dynamics of the system by leveraging time correlations in sensor data.</li><li>It achieves state-of-the-art classification performance, outperforming the BATADAL challenge leader without domain-specific knowledge.</li><li>The hybrid structure maintains computational efficiency, making it broadly applicable and enhances the resilience of CPS.</li></ul>

Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems

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