10th International Congress on Information and Communication Technology

Authors - Prathyush Kiran Holla, Manish M, Purvi Hande, Akshay Anand, Nirmala Devi M
Abstract - Integrated Circuits (IC) allows attackers to insert malicious implants called Hardware Trojans (HT). These Trojans leak information or alter circuit functionality. This threat is particularly critical in IoT devices, where compromised hardware can lead to drastic consequences across networks potentially exposing entire systems to data loss. Over the past decade, numerous Hardware Trojan Detection (HTD) methods have been developed which is crucial for securing IoT ecosystems, where detecting hardware-level threats early can prevent cascade failures. Current HTD techniques still face challenges with detection accuracy, class imbalance handling and high false positive/negative rates. We propose a HTD method using XGBoost, enhanced with focal loss to better handle class imbalance. XGBoost is combined with both graph-based and structural features to achieve higher accuracy compared to using each feature type individually. This approach is particularly valuable for IoT applications, where interconnected systems require robust detection methods. The proposed model, evaluated on an extensive dataset comprising of 41 combinational and sequential benchmark circuits, achieves an impressive accuracy of 98.85%, demonstrating superior performance in HT detection across diverse circuit architectures. Such high accuracy is essential for IoT deployments where false positives can trigger unnecessary disruptions across connected systems, and false negatives can leave critical infrastructure vulnerable to attacks.