SURF: Structural Functional Attack on Logic Locking

By: Prabuddha Chakraborty, Jonathan Cruz, and Swarup Bhunia

Stage: Gate-Level

Summary

SURF is an obfuscation evaluation tool written in C and Python that uses machine learning, structural, and functional analysis to unlock a locked gate-level netlist. From an initial key guess (provided with the aid of SAIL), SURF iteratively produces an exact or approximate key of a locked design with the help of functional simulation and structure-aware greedy optimization technique.

Contact

Prabuddha Chakraborty

Input/Output Interface

Input: Locked Gate-level Netlist, Golden I/O Pairs
Output: Key bits

References

Chakraborty, Prabuddha; Cruz, Jonathan; Bhunia, Swarup

SURF: Joint Structural Functional Attack on Logic Locking Proceedings Article

In: 2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 181-190, 2019.

Abstract | Links | BibTeX

@inproceedings{8741028,

title = {SURF: Joint Structural Functional Attack on Logic Locking},

author = {Prabuddha Chakraborty and Jonathan Cruz and Swarup Bhunia},

doi = {10.1109/HST.2019.8741028},

year  = {2019},

date = {2019-05-01},

booktitle = {2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)},

pages = {181-190},

abstract = {To help protect hardware Intellectual Property (IP) blocks against piracy and reverse engineering, researchers have proposed various obfuscation techniques that aim at hiding design intent and making black-box usage difficult. A dominant form of obfuscation, referred to as logic locking, relies on the insertion of key gates (e.g., XOR/XNOR) at strategic locations in a design followed by logic synthesis. Recently, it has been shown that such an approach leaves predictable structural signatures, which make them susceptible to machine learning (ML) based structural attacks. These attacks are shown to deobfuscate a design by learning the deterministic nature of transformations incorporated by commercial synthesis tools. They are attractive for unraveling the design intent. However, they may not be able to provide a working design. In this paper, we introduce a novel attack on obfuscation techniques, called Structural Functional (SURF) attack, which, for the first time to our knowledge, accomplishes key extraction through scalable functional analysis while leveraging the output of structural attacks. We have developed complete flow and an automatic tool for the attack, which shows promising results. We are able to retrieve, on average, ~90% keybits for obfuscated ISCAS-85 benchmarks (100% in several cases) with > 98% output accuracy. We observe that SURF attack, unlike any known attack, can enable both discovering design intent as well as black-box usage. It is effective for all major variants of logic locking, scalable to large designs, and unlike SAT based attacks, is effective for all design types (e.g., multipliers, where SAT based attacks typically fail).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}