PFS - A Platform for Structural Analysis of Logic Locking Using Machine Learning
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Document typeConference report
Defense date2022-05
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Abstract
Hardware obfuscation or logic locking (LL), relate
to a method of safeguarding hardware intellectual property (IP)
blocks against a variety of attacks, such as, IP theft, reverse
engineering, and malicious alterations. Modern locking strategies
primarily focus on preventing illegal use of a design by limiting
correct functionality; they rarely address preventing extraction of
design secrets through structural analysis. The locking gates are
also vulnerable to removal attempts due to a lack of structural
obfuscation. In this work, we analyze the structural alterations
caused by LL and introduce a new class of attacks (SAIL,
SURF) and associated CAD platform that take advantage of
these artifacts. SAIL can expose the structural vulnerabilities
of a locked design through a systematic set of steps and open
it up for subsequent key-retrieval/reverse-engineering attacks.
SAIL is far stronger than prevalent attacks such as SAT-based
attacks because SAIL does not require an unlocked-IC or golden
I/O responses to carry out the attack. We have also proposed
the SIVA-Metric which can be used to quantify the existing
structural vulnerabilities of a given design. It can be directly
computed without additional training requirements. The SURF
attack leverages the structural analysis output of SAIL to mount
a subsequent key-retrieval attack. Through extensive quantitative
analysis, we have demonstrated the efficacy of the proposed
attacks/metrics.
CitationChakraborty, P. [et al.]. A Platform for Structural Analysis of Logic Locking Using Machine Learning. A: 27th IEEE European Test Symposium (ETS). 2022,
Publisher versionhttps://ieeexplore.ieee.org/xpl/conhome/9810327/proceeding
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