Welcome to the resource topic for 2022/1752
Title:
IsoLock: Thwarting Link-Prediction Attacks on Routing Obfuscation by Graph Isomorphism
Authors: Shaza Elsharief, Lilas Alrahis, Johann Knechtel, Ozgur Sinanoglu
Abstract:Logic locking/obfuscation secures hardware designs from untrusted entities throughout the globalized semiconductor supply chain. Machine learning (ML) recently challenged the security of locking: such attacks successfully captured the locking-induced, structural design modifications to decipher obfuscated gates. Although routing obfuscation eliminates this threat, more recent attacks exposed new vulnerabilities, like link formation, breaking such schemes. Thus, there is still a need for advanced, truly learning-resilient locking solutions.
Here we propose IsoLock, a provably-secure locking scheme that utilizes isomorphic structures which ML models and other structural methods cannot discriminate. Unlike prior work, IsoLock’s security promise neither relies on re-synthesis nor on dedicated sub-circuits. Instead, IsoLock introduces isomorphic key-gate structures within the design via systematic routing obfuscation. We theoretically prove the security of IsoLock against modeling attacks. Further, we lock ISCAS-85 and ITC-99 benchmarks and launch state-of-the-art ML attacks, SCOPE and MuxLink, as well as the Redundancy and SAAM attacks, which only decipher an average of 0–6% of the key, well confirming the resilience of IsoLock. All in all, IsoLock is proposed to break the cycle of “cat and mouse” in locking and attack studies, through a provably-secure locking approach against structural ML attacks.
ePrint: https://eprint.iacr.org/2022/1752
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