UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking

Lilas Alrahis, Satwik Patnaik, Johann Knechtel, Hani Saleh, Baker Mohammad, Mahmoud Al-Qutayri, Ozgur Sinanoglu

Research output: Contribution to journalArticlepeer-review

Abstract

Logic locking aims to protect the intellectual property (IP) of integrated circuit (IC) designs throughout the globalized supply chain. The SAIL attack, based on tailored machine learning (ML) models, circumvents combinational logic locking with high accuracy and is amongst the most potent attacks as it does not require a functional IC acting as an oracle. In this work, we propose UNSAIL, a logic locking technique that inserts key-gate structures with the specific aim to confuse ML models like those used in SAIL. More specifically, UNSAIL serves to prevent attacks seeking to resolve the structural transformations of synthesis-induced obfuscation, which is an essential step for logic locking. Our approach is generic; it can protect any local structure of key-gates against such ML-based attacks in an oracle-less setting. We develop a reference implementation for the SAIL attack and launch it on both traditionally locked and UNSAIL-locked designs. For SAIL, two ML models have been proposed (which we implement accordingly), namely a change-prediction model and a reconstruction model; the change-prediction model is used to determine which key-gate structures to restore using the reconstruction model. Our study on benchmarks ranging from the ISCAS-85 and ITC-99 suites to the OpenRISC Reference Platform System-on-Chip (ORPSoC) confirms that UNSAIL degrades the accuracy of the change-prediction model and the reconstruction model by an average of 20.13 and 17 percentage points (pp), respectively. When the aforementioned models are combined, which is the most powerful scenario for SAIL, UNSAIL reduces the attack accuracy of SAIL by an average of 11pp. We further demonstrate that UNSAIL thwarts other oracle-less attacks, i.e., SWEEP and the redundancy attack, indicating the generic nature and strength of our approach. Detailed layout-level evaluations illustrate that UNSAIL incurs minimal area and power overheads of 0.26% and 0.61%, respectively, on the million-gate ORPSoC design.

Original languageEnglish (US)
Article number9350294
Pages (from-to)2508-2523
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Volume16
DOIs
StatePublished - 2021

Keywords

  • IP protection
  • Logic locking
  • hardware obfuscation
  • hardware security
  • machine learning

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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