Untrusted third parties in commercial-off-the-shelf (COTS) printed circuit board (PCB) supply chains may poison PCBs with hardware, firmware, and software implants. Hence, we focus on detection of malicious implants in PCBs. State-of-the-art hardware Trojan detection methods require a golden PCB system/model to detect malicious implants and do not scale to large-scale COTS PCB systems. We map a COTS PCB system to a graph and propose a golden-free methodology comprising a graph-based mathematical construction on node and edge equivalences, and clustering of identical nodes and paths and validation of hypothesized statistical properties on measured sidechannel data. We evaluate the methodology on a multi-PCB testbed with hierarchically networked PCB devices and several types of Trojans.
ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering