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Structurally Secure Obfuscation: Assessing and Mitigating Structural Vulnerabilities in Circuits Obfuscation
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DOI：10.1145/3772062.png)
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Abstract
En 中文
Because of the globalization of IC manufacturing and to protect IP integrity and confidentiality, circuit obfuscation techniques have been developed. These methods secure the circuit through obfuscation approaches. Recently, advanced machine learning (ML)-based structural attacks have been introduced that employ the structure of the circuit to reverse the obfuscation mechanism. These attacks use ML-based approaches to analyze and neutralize obfuscation schemes without requiring unlocked functional circuits, posing a significant challenge to IP security. To counter ML-based attacks, in this article, we first analyze the sources of structural leakages of the interconnect obfuscation technique, one of the most robust IP protection mechanisms. We conduct a first-of-its-kind analysis of the circuit's netlist graph, obfuscated using interconnect obfuscation, to evaluate its robustness against link prediction techniques. Based on our analysis, we introduce a security assessment tool that evaluates the strength of the obfuscation technique in omitting structural leakages that lead to the success of ML-based attacks. Our assessment tool reveals that previous obfuscation methods fall short of achieving their intended security levels. This leads to our second contribution, which is proposing ML-SafeConnect, an interconnect obfuscation technique that protects the obfuscated substructures by completely eliminating distance-based structural leakages. Using our assessment tool and state-of-the-art ML-based attack, we demonstrate that our obfuscation mechanism surpasses previous interconnect obfuscation techniques in preventing structural leakages. We show that ML-SafeConnect completely thwarts ML-based attacks for all benchmark circuits by decreasing the accuracy of state-of-the-art attacks to below 50%.
Keywords:
Circuit obfuscation
logic locking
interconnect-based obfuscation
structural attacks
machine learning-based attacks
Journal
A
IF:
2
Papers:
112
Citations:
1.2K