An ML-Assisted Golden-Free Hardware Trojan Localization and Detection Approach for Trusted Microelectronics

Hardware Trojans are malicious circuits, hidden in integrated circuits (ICs) which pose a significant threat to security. Detection of hardware Trojans is important to build trust, verify, and make the semiconductor ICs process secure. The existing hardware Trojan detection methods are generally destructive, require intricate comparisons, or require a long time for reverse engineering.In the initial phase of this study, the substitution of supervised hardware Trojan detection methods in ASICs chips is explored with unsupervised approaches, thereby eliminating the dependence on golden references. The Trojan detection uses a ring oscillator (RO) based on NAND as the power monitor. Frequency side channels are computed, and unsupervised classifiers are employed, achieving a commendable accuracy of 93%, comparable to state-of-the-art methods requiring golden data.In the later part of our work, different feature extraction techniques are applied to improve the competence and precision of Trojan detection. Experiments include frequency side-channel extraction with the use of implanted ROs in Basys FPGA, resulting a higher F1 score and accuracy (95\%) in detection of the Trojan. Further, the method has performed equally well for all the Trojan sizes considered (8-bit, 16-bit, and 32-bit) during the performance evaluation with no significant difference and has yielded equal efficiency in detecting even small-sized Trojans.After clustering ICs, a post-processing localization algorithm is proposed to help in identifying and locating hardware Trojan within the chip with reference to the ROs. This algorithm eliminates the requirement of complete reverse engineering of at least one IC from a cluster to figure out if the cluster formed by ML contains Trojan-infected or not 'golden' chips.The proposed algorithm simplifies this process by focusing mainly on the chip portion, where the Trojan is localized.Lastly, the study presents some vulnerability of the clustering model to adversarial attacks in the Trojan detection technique. These attacks reduce significantly the accuracy of chip clustering into the correct cluster up to 50\%. Consequently, we advocate for the integration of adversarial defense mechanisms into the hardware Trojan detection machine learning models, an area we identify as a focus for future research. In conclusion, the presented work enhances unsupervised hardware Trojan detection improvements by presenting new feature extraction techniques and giving a localization algorithm to avoid reverse engineering efforts. The need and importance of adversarial defense mechanisms to make machine learning systems detect Trojans has been highlighted.