Deep Graph Learning for Circuit Deobfuscation

dc.contributor.authorChen, Zhiqianen
dc.contributor.authorZhang, Leien
dc.contributor.authorKolhe, Gauraven
dc.contributor.authorKamali, Hadi Mardanien
dc.contributor.authorRafatirad, Setarehen
dc.contributor.authorPudukotai Dinakarrao, Sai Manojen
dc.contributor.authorHomayoun, Houmanen
dc.contributor.authorLu, Chang-Tienen
dc.contributor.authorZhao, Liangen
dc.date.accessioned2021-11-22T20:29:01Zen
dc.date.available2021-11-22T20:29:01Zen
dc.date.issued2021-05-24en
dc.description.abstractCircuit 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fdata.2021.608286en
dc.identifier.eissn2624-909Xen
dc.identifier.other608286en
dc.identifier.pmid34109310en
dc.identifier.urihttp://hdl.handle.net/10919/106714en
dc.identifier.volume4en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgraph miningen
dc.subjectcircuit deobfuscationen
dc.subjectsatisfiability checkingen
dc.subjectgraph neural networksen
dc.subjectdeep learningen
dc.titleDeep Graph Learning for Circuit Deobfuscationen
dc.title.serialFrontiers in Big Dataen
dc.typeArticle - Refereeden
dc.type.dcmitypetexten

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