LeGO: A Learning-Guided Obfuscation Framework for Hardware IP Protection

Alaql, Abdulrahman; Chattopadhyay, Saranyu; Chakraborty, Prabuddha; Hoque, Tamzidul; Bhunia, Swarup

doi:10.1109/tcad.2021.3075939

Cited by 12 publications

(1 citation statement)

References 37 publications

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“…This will lead to the revealing of inner details about the design, where the attacker can steal the intellectual property, or even improve it on their own product to achieve illegal advantage. The threat of reverse engineering is discussed in details in [30].…”

Section: Reverse Engineering (Re)mentioning

confidence: 99%

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Pan

Mishra

2022

IEEE Access

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Electronic systems rely on efficient hardware, popularly known as system-on-chip (SoC), to support its core functionalities. A typical SoC consists of diverse components gathered from third-party vendors to reduce SoC design cost and meet time-to-market constraints. Unfortunately, the participation of third-party companies in global supply chain introduces potential security vulnerabilities. There is a critical need to efficiently detect and mitigate hardware vulnerabilities. Machine learning has been successfully used in hardware security verification as well as development of effective countermeasures. There are recent surveys on hardware Trojan detection using machine learning. To the best of our knowledge, there are no comprehensive surveys on utilization of machine learning techniques for detection and mitigation of a wide variety of hardware vulnerabilities including malicious implants (e.g., hardware Trojans), sidechannel leakage, reverse engineering, and supply-chain vulnerabilities (e.g., counterfeiting, overbuilding and recycling). In this paper, we provide a comprehensive survey of hardware vulnerability analysis using machine learning techniques. Specifically, we discuss how existing approaches effectively utilize machine learning algorithms for hardware security verification using simulation-based validation, formal verification as well as side-channel analysis.

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Section: Reverse Engineering (Re)mentioning

confidence: 99%