VIPR-PCB: a machine learning based golden-free PCB assurance framework

Printed circuit boards (PCBs) form an integral part of the electronics life cycle by providing mechanical support and electrical connections to microchips and discrete electronic components. PCBs follow a similar life cycle as microchips and are vulnerable to similar assurance issues. Malicious design alterations, i.e., hardware Trojan attacks, have emerged as a major threat to PCB assurance. Board-level Trojans are extremely challenging to detect due to (1) the lack of golden or reference models in most use cases, (2) potentially unbounded attack space, and (3) the growing complexity of commercial PCB designs. Existing PCB inspection techniques (e.g., optical and electrical) do not scale to large volume and are expensive, time-consuming, and often not reliable in covering diverse Trojan space. To address these issues, in this paper, we present VIPR-PCB, a board-level Trojan detection framework that employs a machine learning (ML) model to learn Trojan signatures in functional and structural space and uses a trained model to discover Trojans in suspect PCB designs with high fidelity. Using extensive evaluation with 10 open-source PCB designs and a wide variety of Trojan instances, we demonstrate that VIPR-PCB can achieve over 98% accuracy and is even capable of detecting Trojans in partially-recovered PCB designs.