# Deceptive Logic Locking for Hardware Integrity Protection against Machine Learning Attacks

arXiv: https://arxiv.org/abs/2107.08695 · HTML: https://mlsanity.com/paper/2107.08695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [The Cost of Application-Class Processing: Energy and Performance Analysis of a Linux-ready 1.7GHz 64bit RISC-V Core in 22nm FDSOI Technology](https://mlsanity.com/paper/1904.05442/) (2019)
- [SAIL: Machine Learning Guided Structural Analysis Attack on Hardware Obfuscation](https://mlsanity.com/paper/1809.10743/) (2018)
- [Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach](https://mlsanity.com/paper/2011.10389/) (2020)
- [GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking](https://mlsanity.com/paper/2012.05948/) (2020)
- [UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking](https://mlsanity.com/paper/2012.14938/) (2020)
- [Logic Locking for Secure Outsourced Chip Fabrication: A New Attack and Provably Secure Defense Mechanism](https://mlsanity.com/paper/1703.10187/) (2017)
- [LUT-Lock: A Novel LUT-based Logic Obfuscation for FPGA-Bitstream and ASIC-Hardware Protection](https://mlsanity.com/paper/1804.11275/) (2018)
- [Threats on Logic Locking: A Decade Later](https://mlsanity.com/paper/1905.05896/) (2019)
- [NNgSAT: Neural Network guided SAT Attack on Logic Locked Complex Structures](https://mlsanity.com/paper/2009.02208/) (2020)
- [ASSURE: RTL Locking Against an Untrusted Foundry](https://mlsanity.com/paper/2010.05344/) (2020)
- [Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit Attack](https://mlsanity.com/paper/1803.03332/) (2018)
- [SRCLock: SAT-Resistant Cyclic Logic Locking for Protecting the Hardware](https://mlsanity.com/paper/1804.09162/) (2018)
- [ATPG-Guided Fault Injection Attacks on Logic Locking](https://mlsanity.com/paper/2007.10512/) (2020)
- [InterLock: An Intercorrelated Logic and Routing Locking](https://mlsanity.com/paper/2009.02206/) (2020)
- [Modeling Techniques for Logic Locking](https://mlsanity.com/paper/2009.10131/) (2020)
- [Scalable Attack-Resistant Obfuscation of Logic Circuits](https://mlsanity.com/paper/2010.15329/) (2020)
- [A Novel Topology-Guided Attack and Its Countermeasure Towards Secure Logic Locking](https://mlsanity.com/paper/2006.05930/) (2020)

## Built on by (most cited first, 3 of 3)

- [Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://mlsanity.com/paper/2211.16495/) (2022)
- [Security Closure of IC Layouts Against Hardware Trojans](https://mlsanity.com/paper/2211.07997/) (2022)
- [TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans](https://mlsanity.com/paper/2405.05590/) (2024)

## Abstract (arXiv)

Logic locking has emerged as a prominent key-driven technique to protect the integrity of integrated circuits. However, novel machine-learning-based attacks have recently been introduced to challenge the security foundations of locking schemes. These attacks are able to recover a significant percentage of the key without having access to an activated circuit. This paper address this issue through two focal points. First, we present a theoretical model to test locking schemes for key-related structural leakage that can be exploited by machine learning. Second, based on the theoretical model, we introduce D-MUX: a deceptive multiplexer-based logic-locking scheme that is resilient against structure-exploiting machine learning attacks. Through the design of D-MUX, we uncover a major fallacy in existing multiplexer-based locking schemes in the form of a structural-analysis attack. Finally, an extensive cost evaluation of D-MUX is presented. To the best of our knowledge, D-MUX is the first machine-learning-resilient locking scheme capable of protecting against all known learning-based attacks. Hereby, the presented work offers a starting point for the design and evaluation of future-generation logic locking in the era of machine learning.