Adversarially Robust Hardware Trojan Detection with Synthetic Data Augmentation
As semiconductor manufacturing becomes increasingly outsourced to untrusted entities, Hardware Trojan (HT) attacks pose a critical threat to the security and reliability of modern integrated circuits. Machine learning models have improved the effectiveness of HT detection using Ring Oscillator Network (RON) side-channel data, yet recent work shows that these models are highly vulnerable to adversarial attacks. This paper evaluates the robustness of the Support Vector Machine (SVM) classifier, a leading algorithm in state-of-the-art HT detection frameworks, under gradient-based adversarial attacks. The proposed work demonstrates that high nominal accuracy does not ensure security against these attacks, which can reduce recall to zero. To strengthen resilience, three data-augmentation methods are investigated: SMOTE, Conditional Tabular Generative Adversarial Network (CTGAN), and Tabular Variational Autoencoder (TVAE). TVAE produces high-fidelity synthetic samples and substantially improves robustness, maintaining over 91% accuracy for nominal performance and over 88% accuracy under strong adversarial perturbations that cause a 100% attack success rate for the surrogate model. The results highlight the need to reframe hardware security evaluations beyond nominal accuracy toward adversarial robustness.