Consistent Interpretation of Ensemble Classifiers in Trojan-Horse Detection

Abstract

Hardware trojan classification/detection systems (HTDs) based on machine or deep learning have recently been proven to be effective. However, the existence of irrelevant features as well as class imbalance reduces the effectiveness of these models. To address these issues, this work describes a hardware trojan detection method based on gate-level net-list structural features. To begin with, SMOTE-Tomek is used for data augmentation. The best features are then selected using a hybrid feature selection technique that combines the filter and wrapper. The results show that using the optimal features and tuned parameters, KNORA-U and KNORA-E, dynamic ensemble classifiers, outperform existing techniques with area under the receiver operating characteristic curve (AUC-ROC) values of 0.988 and 0.982, respectively. The circuit and systems (CAS) lab dataset is used to analyze these evaluations. Furthermore, knowing the details of the prediction is extremely crucial for the model’s transparency and generalizability. As a result, when using a model agonistic framework such as SHapley Additive exPlanations (SHAP), it is proved that, in addition to other features, the number of references is consistent across models and has a significant impact on prediction. Due to consistent interpretations, this methodology strengthens the hardware security professionals’ trust in HTDs.

Keywords

• Computational time
• consistency
• local accuracy
• hardware security
• SHAP