Hardware trojan identification using machine learning-based classification

Abstract

As Hardware Trojans (HTs) emerges as the new threats for the integrated circuits (ICs), methods for identifying and detecting HTs have been widely researched and proposed. Identifying the HTs are important because it can assist in developing proper techniques for inserting and detecting the treat in ICs. One of the recent method of identifying and detecting HTs in ICs is classification using machine learning (ML) algorithm. There is still lack of machine learning-based classification for HTs identification. Thus, a three type of ML based classification includes Decision Tree (DT), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are proposed for HTs identification. The dataset is based from the Trust-Hub. In order to improve the classification accuracy, the HTs are discretized based on their dominant attributes. The discretized HTs are classified using three machine learning algorithms. The results show that the DT and KNN learnt model are able to correctly predict about 83% of the test data.