| Title | A Novel Modeling-Attack Resilient Arbiter-PUF Design |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ebrahimabadi, Mohammad, Younis, Mohamed, Lalouani, Wassila, Karimi, Naghmeh |
| Conference Name | 2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID) |
| Keywords | Adversary Models, arbiter PUF, Human Behavior, machine learning, machine learning algorithms, Metrics, physical unclonable function, Prediction algorithms, Predictive models, pubcrawl, PUF modeling, Resiliency, Scalability, Training, Very large scale integration |
| Abstract | Physically Unclonable Functions (PUFs) have been considered as promising lightweight primitives for random number generation and device authentication. Thanks to the imperfections occurring during the fabrication process of integrated circuits, each PUF generates a unique signature which can be used for chip identification. Although supposed to be unclonable, PUFs have been shown to be vulnerable to modeling attacks where a set of collected challenge response pairs are used for training a machine learning model to predict the PUF response to unseen challenges. Challenge obfuscation has been proposed to tackle the modeling attacks in recent years. However, knowing the obfuscation algorithm can help the adversary to model the PUF. This paper proposes a modeling-resilient arbiter-PUF architecture that benefits from the randomness provided by PUFs in concealing the obfuscation scheme. The experimental results confirm the effectiveness of the proposed structure in countering PUF modeling attacks. |
| DOI | 10.1109/VLSID51830.2021.00026 |
| Citation Key | ebrahimabadi_novel_2021 |
A Novel Modeling-Attack Resilient Arbiter-PUF Design