Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

Authors: Dominik Sisejkovic, Lennart M. Reimann, Elmira Moussavi, Farhad Merchant, Rainer Leupers
Published: 2021-07-05 | Updated: 2021-11-23

Source: https://arxiv.org/abs/2107.01915

PDF: https://arxiv.org/pdf/2107.01915

AIにより推定されたラベル

攻撃の評価 機械学習の応用 機械学習技術

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Abstract

In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.