Hardware trojan detection in ISCAS-89 circuits using supervised machine learning algorithms

Hardware Trojans pose a substantial risk to integrated circuits (ICs). It is critical to discover these unauthorised alterations within the first stages of manufacture, if not before mass production begins. Hardware The impact of Hardware Trojans on ISCAS'89 benchmark circuits varies depending on the Trojan design and how it affects the circuit's behaviour. As these circuitsget more complex, there is a rising necessity for automatedand scalable Trojan detection methods. Traditional detection approaches, such as logic tests and post-verification, need previous knowledge of Trojans, which might be difficult to get. In response to this problem, our research focuses on application of supervised machine learning methods in order to efficiently train the models which are designed to detect Trojans in ISCAS-89 circuits, even during the early phases of manufacture, hence improving the security and dependability of integrated circuits in the face of changing threats. The experimental results on ISCAS'89 benchmark circuits indicates that the suggested model is capable of identifying hardware trojanswith a reasonable level of accuracy, reaching up to 99%