期刊
PROCEEDINGS OF THE 59TH ACM/IEEE DESIGN AUTOMATION CONFERENCE, DAC 2022
卷 -, 期 -, 页码 769-774出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3489517.3530541
关键词
logic locking; RTL; IP protection; machine learning; deobfuscation
类别
资金
- ONR [N00014-18-1-2058]
- NSF [1526405]
- NYU Center for Cybersecurity
- NYUAD Center for Cybersecurity
This paper investigates the resilience of the state-of-the-art RTL locking method ASSURE against machine-learning attacks. Two machine-learning reinforced RTL locking schemes are proposed based on the lessons learned. ML-driven security metrics are also developed to evaluate the schemes against the latest ML-based attacks.
Various logic-locking schemes have been proposed to protect hardware from intellectual property piracy and malicious design modifications. Since traditional locking techniques are applied on the gate-level netlist after logic synthesis, they have no semantic knowledge of the design function. Data-driven, machine-learning (ML) attacks can uncover the design flaws within gate-level locking. Recent proposals on register-transfer level (RTL) locking have access to semantic hardware information. We investigate the resilience of ASSURE, a state-of-the-art RTL locking method, against ML attacks. We used the lessons learned to derive two ML-resilient RTL locking schemes built to reinforce ASSURE locking. We developed ML-driven security metrics to evaluate the schemes against an RTL adaptation of the state-of-the-art, ML-based SnapShot attack.
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