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Lookup NU author(s): Dr Farhad Merchant
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Logic locking is a prominent technique to protect the integrity of hardware designs throughout the integrated circuit design and fabrication flow. However, in recent years, the security of locking schemes has been thoroughly challenged by the introduction of various deobfuscation attacks. As in most research branches, deep learning is being introduced in the domain of logic locking as well. Therefore, in this article we present SnapShot, a novel attack on logic locking that is the first of its kind to utilize artificial neural networks to directly predict a key bit value from a locked synthesized gate-level netlist without using a golden reference. Hereby, the attack uses a simpler yet more flexible learning model compared to existing work. Two different approaches are evaluated. The first approach is based on a simple feedforward fully connected neural network. The second approach utilizes genetic algorithms to evolve more complex convolutional neural network architectures specialized for the given task. The attack flow offers a generic and customizable framework for attacking locking schemes using machine learning techniques. We perform an extensive evaluation of SnapShot for two realistic attack scenarios, comprising both reference combinational and sequential benchmark circuits as well as silicon-proven RISC-V core modules. The evaluation results show that SnapShot achieves an average key prediction accuracy of 82.60% for the selected attack scenario, with a significant performance increase of 10.49 percentage points compared to the state of the art. Moreover, SnapShot outperforms the existing technique on all evaluated benchmarks. The results indicate that the security foundation of common logic locking schemes is built on questionable assumptions. Based on the lessons learned, we discuss the vulnerabilities and potentials of logic locking uncovered by SnapShot. The conclusions offer insights into the challenges of designing future logic locking schemes that are resilient to machine learning attacks.
Author(s): Sisejkovic D, Merchant F, Reimann L, Srivastava H, Hallawa A, Leupers R
Publication type: Article
Publication status: Published
Journal: ACM Journal on Emerging Technologies in Computing Systems
Year: 2021
Volume: 17
Issue: 3
Print publication date: 01/07/2021
Online publication date: 11/05/2021
Acceptance date: 01/10/2020
ISSN (print): 1550-4832
ISSN (electronic): 1550-4840
Publisher: Association for Computing Machinery
URL: https://doi.org/10.1145/3431389
DOI: 10.1145/3431389
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