Hardware Trojan Detection using Supervised Machine Learning

As the Integrated circuits (IC) production has been increased, the insertion of malicious hardware units is common. These malicious hardware units are called the hardware trojans. Third-party vendors are responsible for hardware trojan insertion in the circuits. In this paper, a technique which detect the circuits which are affected with the hardware trojan or not is explored. The usage of supervised machine learning technique, the random forest algorithm, helps us to detect the presence of the malicious hardware Trojans. This technique uses five features which are extracted from the circuits using the Gate level netlist. These features are identified for each nets of the circuits. Thus, by using the random forest classifier for the categorization, the true positive (TP), false positive (FP), true negative (TN), false negative (FP) can also be obtained. Also, the parameters like precision, recall, accuracy and f-measure are calculated for the ISCAS'85 benchmark circuits. The computed results report an increase in accuracy.