Abstract
Machine Learning (ML) has increasingly found its place in a repertoire of tools used in most domains, from speech recognition to self-driving cars. One example of this growth is the prevalence of this technology in hardware security and its various sub-fields. Chapter 10 presents a machine learning survey on hardware security, particularly in two sub-fields: Trojan detection and side-channel analysis (SCA). This chapter evaluates a variety of works, which all use machine learning techniques to augment various hardware security frameworks or even create new ones entirely. The survey consists of works that seek to analyze the beneficial effects of machine learning better, infer new relationships between side-channel measurements, and extract sensitive information. The other works use machine learning to develop real-time hardware Trojan detection frameworks on a many-core platform, which have been rarely investigated in prior works. We express here the major findings of these five works and how they are relevant in the larger scope of the hardware security domain. The implementation strategies and their results are investigated. However, Chap. 10 does not investigate other sub-fields of hardware security where machine learning is deployed for fault injection techniques, verification, system-level PCB attacks, etc.
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Farahmandi, F., Rahman, M.S., Rajendran, S.R., Tehranipoor, M. (2023). CAD for Machine Learning in Hardware Security. In: CAD for Hardware Security . Springer, Cham. https://doi.org/10.1007/978-3-031-26896-0_10
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DOI: https://doi.org/10.1007/978-3-031-26896-0_10
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