Estimating the Circuit De-obfuscation Runtime based on Graph Deep Learning

Chen, Zhiqian; Kolhe, Gaurav; Rafatirad, Setareh; Lu, Chang-Tien; D, Sai Manoj P; Homayoun, Houman; Zhao, Liang

doi:10.23919/date48585.2020.9116544

Cited by 18 publications

(13 citation statements)

References 21 publications

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“…Although there have been advancements in logic locking techniques [9], researchers have been able to ind the vulnerabilities in many of the most prominent obfuscation schemes. Attacks such as CycSAT, SMT-Solver [4,6,8,32] show that by considering just a few conditions and pre-processing steps, designs obfuscated with cyclic obfuscation can be reverse-engineered. The state-of-the-art attack SFLL-HD [54] has also been successfully defeated using FALL-attack [41] which does not require an oracle IC to ind the correct key making the attack even more feasible.…”

Section: Post-sat Obfuscation and Challengesmentioning

confidence: 99%

Breaking the Design and Security Trade-off of Look-up-table–based Obfuscation

Kolhe

Sheaves

et al. 2022

ACM Trans. Des. Autom. Electron. Syst.

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Logic locking and Integrated Circuit (IC) camouflaging are the most prevalent protection schemes that can thwart most hardware security threats. However, the state-of-the-art attacks, including Boolean Satisfiability (SAT) and approximation-based attacks, question the efficacy of the existing defense schemes. Recent obfuscation schemes have employed reconfigurable logic to secure designs against various hardware security threats. However, they have focused on specific design elements such as SAT hardness. Despite meeting the focused criterion such as security, obfuscation incurs additional overheads, which are not evaluated in the present works. This work provides an extensive analysis of Look-Up-Table (LUT)-based obfuscation by exploring several factors such as LUT technology, size, number of LUTs, and replacement strategy as they have a substantial influence on Power-Performance-Area (PPA) and Security (PPA/S) of the design. We show that using large LUT makes LUT-based obfuscation resilient to hardware security threats. However, it also results in enormous design overheads beyond practical limits. To make the reconfigurable logic obfuscation efficient in terms of design overheads, this work proposes a novel LUT architecture where the security provided by the proposed primitive is superior to that of the traditional LUT-based obfuscation. Moreover, we leverage the security-driven design flow, which uses off-the-shelf industrial EDA tools to mitigate the design overheads further while being non-disruptive to the current industrial physical design flow. We empirically evaluate the security of the LUTs against state-of-the-art obfuscation techniques in terms of design overheads and SAT-attack resiliency. Our findings show that the proposed primitive significantly reduces both area and power by a factor of 8 × and 2 ×, respectively, without compromising security.

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Section: Post-sat Obfuscation and Challengesmentioning

confidence: 99%

Breaking the Design and Security Trade-off of Look-up-table–based Obfuscation

Kolhe

Sheaves

et al. 2022

ACM Trans. Des. Autom. Electron. Syst.

Self Cite

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“…The latest efforts in the logic locking community have been invested in challenging the security properties of locking schemes using ML. Recent works were able to efficiently attack pre-and post-SAT schemes [20]- [28]. The introduction of ML-based tools for the security analysis of logic locking has opened up a new chapter in the design of locking schemes and attacks, thereby initiating the start of the post-ML lockingscheme era.…”

Section: B the Advent Of Machine Learningmentioning

confidence: 99%

“…Deobfuscation Runtime Prediction: Apart from ML-based attacks, machine learning has also found its way into other aspects of logic locking. A recent work has designed a framework named ICNet for the prediction of the key-retrieval runtime for SAT-based attacks using graph neural networks [28]. The framework obtains the predicted deobfuscation runtime based on the characteristics of the circuit topology.…”

Section: Other Applications Of MLmentioning

confidence: 99%

Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

Sisejkovic,

Reimann,

Moussavi

et al. 2021

Preprint

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In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.

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“…GNNs have been evaluated on circuit-related problems as well [42]. In particular, GNNs have been used for attack time estimation on logic locking [10]. In this sense, GNNs offer a potentially fruitful research ground in the domain of hardware security.…”

Section: Limitations and Research Opportunitiesmentioning

confidence: 99%

Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach

Sisejkovic,

Merchant,

Reimann

et al. 2020

Preprint

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Logic locking is a prominent technique to protect the integrity of hardware designs throughout the integrated circuit design and fabrication flow. However, in recent years, the security of locking schemes has been thoroughly challenged by the introduction of various deobfuscation attacks. As in most research branches, deep learning is being introduced in the domain of logic locking as well. Therefore, in this paper we present SnapShot: a novel attack on logic locking that is the first of its kind to utilize artificial neural networks to directly predict a key bit value from a locked synthesized gate-level netlist without using a golden reference. Hereby, the attack uses a simpler yet more flexible learning model compared to existing work. Two different approaches are evaluated. The first approach is based on a simple feedforward fully connected neural network. The second approach utilizes genetic algorithms to evolve more complex convolutional neural network architectures specialized for the given task. The attack flow offers a generic and customizable framework for attacking locking schemes using machine learning techniques. We perform an extensive evaluation of SnapShot for two realistic attack scenarios, comprising both reference combinational and sequential benchmark circuits as well as silicon-proven RISC-V core modules. The evaluation results show that SnapShot achieves an average key prediction accuracy of 82.60% for the selected attack scenario, with a significant performance increase of 10.49 percentage points compared to the state of the art. Moreover, SnapShot outperforms the existing technique on all evaluated benchmarks. The results indicate that the security foundation of common logic locking schemes is build on questionable assumptions. Based on the lessons learned, we discuss the vulnerabilities and potentials of logic locking uncovered by SnapShot. The conclusions offer insights into the challenges of designing future logic locking schemes that are resilient to machine learning attacks.CCS Concepts: • Security and privacy → Security in hardware;

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Estimating the Circuit De-obfuscation Runtime based on Graph Deep Learning

Cited by 18 publications

References 21 publications

Breaking the Design and Security Trade-off of Look-up-table–based Obfuscation

Breaking the Design and Security Trade-off of Look-up-table–based Obfuscation

Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach

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