Reevaluating graph-neural-network-based runtime prediction of sat-based circuit deobfuscation

HIGHLIGHTS

• who: Guangwei Zhao and Kaveh Shamsi from the Department of Electrical and Computer Engineering, University of Texas at Dallas have published the article: Reevaluating Graph-Neural-Network-Based Runtime Prediction of SAT-Based Circuit Deobfuscation, in the Journal: Cryptography 2022, 6, 60. of /2022/
• what: The authors explore whether GCN models truly understand/capture the structural/functional sources of deobfuscation hardness. The authors propose to overcome this limitation by proposing a set of circuit features motivated by block-cipher design principles. The authors develop a novel synthetic circuit benchmark set of Substitution-Permutation Networks (SPN . . .
  

   

  

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