| Summary: | Recently, there are various methods for detecting the hardware trojans (HTs) in the integrated circuits (ICs). The circuit's logic representations of different types, structures, and functional characteristics should be different. Each type of circuit has its' own performance characteristics according to its' purpose. However, the traditional HTs detection methods adopt the same approach to deal with the multi-purpose hardware trojan. At the same time, as the scale of integrated circuits growing, the structures are more complex, and the functions are more refined. The current situation makes the traditional HTs detection methods weaker and even unfeasible. Therefore, we propose an HTs classified locating method based on machine learning, named ML-HTCL, which belongs to the static detection and locating method. In ML-HTCL, different purpose HTs were represented by different features. Due to the different features, the ML-HTCL employs the multi-layer BP neural network for the control signal type HTs and the one-class SVM for the information leakage HTs, respectively. To deal with the HTs completely, the boundary nets are considered for all data set, while those were ignored by the most existing methods due to the high detection error rate. After detection, the HTs' precise locations were achieved. To evaluate the ML-HTCL, 17 gate-level netlist benchmarks are used for training and testing by leave-one-out cross-validation. From the results, the ML-HTCL reaches 85.05% of TPR and 73.91% of TNR for all kinds of HTs, which performs better than the most existing methods.
|
|---|
