Publication

Deep Graph Learning for Circuit Deobfuscation

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Last modified
  • 05/20/2025
Type of Material
Authors
    Zhiqian Chen, Mississippi State UniversityLei Zhang, Virginia Polytechnic Institute and State UniversityGaurav Kolhe, University of California, DavisHadi Mardani Kamali, George Mason UniversitySetareh Rafatirad, University of California, DavisSai Manoj Pudukotai Dinakarrao, George Mason UniversityHouman Homayoun, Emory UniversityChang-Tien Lu, Virginia Polytechnic Institute and State UniversityLiang Zhao, Emory University
Language
  • English
Date
  • 2021-05-24
Publisher
  • Frontiers Media S.A
Publication Version
Copyright Statement
  • © 2021 Chen, Zhang, Kolhe, Kamali, Rafatirad, Pudukotai Dinakarrao, Homayoun, Lu and Zhao.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 4
Start Page
  • 608286
End Page
  • 608286
Abstract
  • Circuit obfuscation is a recently proposed defense mechanism to protect the intellectual property (IP) of digital integrated circuits (ICs) from reverse engineering. There have been effective schemes, such as satisfiability (SAT)-checking based attacks that can potentially decrypt obfuscated circuits, which is called deobfuscation. Deobfuscation runtime could be days or years, depending on the layouts of the obfuscated ICs. Hence, accurately pre-estimating the deobfuscation runtime within a reasonable amount of time is crucial for IC designers to optimize their defense. However, it is challenging due to (1) the complexity of graph-structured circuit; (2) the varying-size topology of obfuscated circuits; (3) requirement on efficiency for deobfuscation method. This study proposes a framework that predicts the deobfuscation runtime based on graph deep learning techniques to address the challenges mentioned above. A conjunctive normal form (CNF) bipartite graph is utilized to characterize the complexity of this SAT problem by analyzing the SAT attack method. Multi-order information of the graph matrix is designed to identify the essential features and reduce the computational cost. To overcome the difficulty in capturing the dynamic size of the CNF graph, an energy-based kernel is proposed to aggregate dynamic features into an identical vector space. Then, we designed a framework, Deep Survival Analysis with Graph (DSAG), which integrates energy-based layers and predicts runtime inspired by censored regression in survival analysis. Integrating uncensored data with censored data, the proposed model improves the standard regression significantly. DSAG is an end-to-end framework that can automatically extract the determinant features for deobfuscation runtime. Extensive experiments on benchmarks demonstrate its effectiveness and efficiency.
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  • Computer Science

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