DECOR: Enhancing Logic Locking Against Machine Learning-Based Attacks

Alapfogalmak

DECOR is a randomized algorithm-based method that significantly decreases the correlation between locked circuit netlist and correct key values, enhancing resilience against ML-based attacks.

Kivonat

Logic locking (LL) has been a crucial measure for IC protection, but recent ML-based attacks have exposed vulnerabilities. DECOR introduces a generic LL enhancement method based on randomized alterations to the locked circuit function. By strategically modifying UDC cofactors, DECOR confuses ML models without affecting legal users' access. Experimental results show that DECOR-XBI and DECOR-SARLock reduce key prediction accuracy to around 50%, providing significant security improvements. The area overhead varies across benchmark circuits but remains acceptable for large systems. PCA analysis demonstrates the effectiveness of DECOR in decorrelating features from labels in training data sets.

Statisztikák

Numerical results show that the proposed method can efficiently degrade the accuracy of state-of-the-art ML-based attacks down to around 50%, resulting in negligible advantage versus random guessing.
Previous efforts to robustify LL against ML-based attacks have mostly focused on making a set of localized, predetermined transformations of the circuit structure with the goal of reducing the correlation between the circuit structure and the correct key.
ML-based attacks consist of two phases, namely, training and inference. The training phase includes three steps: reference netlist generation, training data extraction, and model training.

Idézetek

"DECOR introduces a generic LL enhancement method based on randomized alterations to the locked circuit function."
"Experimental results show that DECOR-XBI and DECOR-SARLock reduce key prediction accuracy to around 50%."

Főbb Kivonatok

DECOR

by Yinghua Hu,K... : arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01789.pdf

Mélyebb kérdések

How does DECOR compare with other logic locking enhancement methods in terms of security effectiveness

DECOR stands out among other logic locking enhancement methods in terms of security effectiveness due to its ability to significantly reduce the correlation between the circuit structure and the correct key. By introducing randomized alterations to the locked circuit function, DECOR confuses machine learning-based attacks by creating many-to-one and one-to-many mapping scenarios in the training data set. This approach makes it challenging for attackers to predict the correct key accurately, thereby enhancing the resilience of any logic locking scheme against such attacks.

What are potential drawbacks or limitations of using randomized algorithms like DECOR for logic locking enhancements

One potential drawback of using randomized algorithms like DECOR for logic locking enhancements is the possibility of increased area overhead. Implementing DECOR may lead to additional complexity in circuit design, resulting in larger chip area requirements. Moreover, as randomized algorithms introduce randomness into the process, there could be challenges in ensuring consistent and predictable outcomes across different implementations or iterations. Additionally, fine-tuning parameters or optimizing randomization strategies may require additional computational resources and expertise.

How can advancements in machine learning algorithms impact the effectiveness of methods like DECOR in securing integrated circuits

Advancements in machine learning algorithms can impact the effectiveness of methods like DECOR in securing integrated circuits by influencing attack capabilities and defense mechanisms. As ML algorithms evolve with improved accuracy and efficiency, attackers may develop more sophisticated techniques to overcome enhanced security measures like DECOR. On the other hand, advancements in ML can also benefit defenders by enabling them to adapt their strategies based on evolving threats and vulnerabilities identified through advanced analytics and pattern recognition capabilities provided by machine learning models. Continuous research and development are essential to stay ahead of emerging threats leveraging ML technologies for attacking integrated circuits' security measures like logic locking enhancements.