Evaluating the Vulnerability of Hiding Techniques in Cyber-Physical Systems Against Deep Learning-Based Side-Channel Attacks

(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, which restricts the implementation of complex countermeasures. Traditional countermeasures such as hiding techniques attempt to obscure power consumption patterns, but deep learning models have challenged their effectiveness. This study evaluates the vulnerability of dummy power traces against deep learning-based SCAs. (2) Methods: A power trace dataset was generated using a QEMU and GDB-based simulation environment, integrating dummy traces to obfuscate execution signatures. Deep learning models—RNN, Bi-RNN, and MLP—were employed to assess classification accuracy. (3) Results: The models trained with dummy traces achieved higher classification accuracy, with the MLP model reaching 97.81% accuracy and an F1-score of 97.77%. Despite the added complexity, deep learning models effectively distinguished real and dummy traces, revealing limitations in existing hiding techniques. (4) Conclusions: These findings highlight the need for adaptive countermeasures against AI-driven SCAs. Future research should explore dynamic obfuscation techniques, adversarial training, and broader cryptographic algorithm evaluations. This study underscores the urgency of evolving security paradigms to counteract AI-powered attacks.