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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. 

Designers use third-party intellectual property (IP) cores and outsource various steps in the integrated circuit (IC) design and manufacturing flow. As a result, security vulnerabilities have been rising. This is forcing IC designers and end users to re-evaluate their trust in ICs. If attackers get hold of an unprotected IC, they can reverse engineer the IC and pirate the IP. Similarly, if attackers get hold of a design, they can insert malicious circuits or take advantage of “backdoors” in a design. Unintended design bugs can also result in security weaknesses. This tutorial paper provides an introduction to the domain of hardware security through two pedagogical examples of hardware security problems. The first is a walk-through of the scan chain-based side channel attack. The second is a walk-through of logic locking of digital designs. The tutorial material is accompanied by open access digital resources that are linked in this article. 

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call for security-aware logic synthesis. We propose ALMOST, a framework for adversarial learning to mitigate oracle-less ML attacks via synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe generator, employing adversarially trained models that can predict state-of-the-art attacks’ accuracies over wide ranges of recipes and key-gate localities. Experiments on ISCAS benchmarks confirm the attacks’ accuracies drops to around 50% for ALMOST-synthesized circuits, all while not undermining design optimization. 

We present novel material-level countermeasures in the form of watermarking and machine learning-based solutions that biochip companies can effectively utilize to secure their products against malicious and intellectual property (IP) theft attacks.