Hardware Trust and Assurance through Reverse Engineering: A Tutorial and Outlook from Image Analysis and Machine Learning Perspectives
Ulbert J. Botero, Ronald S. Wilson, Hangwei Lu, Mir Tanjidur Rahman, Mukhil A. Mallaiyan, Fatemeh Ganji, Navid Asadizanjani, Mark Tehranipoor, Damon L. Woodard, Domenic Forte
Abstract
In the context of hardware trust and assurance, reverse engineering has been often considered as an illegal action. Generally speaking, reverse engineering aims to retrieve information from a product, i.e., integrated circuits (ICs) and printed circuit boards (PCBs) in hardware security-related scenarios, in the hope of understanding the functionality of the device and determining its constituent components. Hence, it can raise serious issues concerning Intellectual Property (IP) infringement, the (in)effectiveness of security-related measures, and even new opportunities for injecting hardware Trojans. Ironically, reverse engineering can enable IP owners to verify and validate the design. Nevertheless, this cannot be achieved without overcoming numerous obstacles that limit successful outcomes of the reverse engineering process. This article surveys these challenges from two complementary perspectives: image processing and machine learning. These two fields of study form a firm basis for the enhancement of efficiency and accuracy of reverse engineering processes for both PCBs and ICs. In summary, therefore, this article presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives.