Abstract
Fake integrated circuit (IC) chips are in circulation on the market, which is considered a serious threat in the era of the Internet of Things (IoTs). A physically unclonable function (PUF) is expected to be a fundamental technique to separate the fake IC chips from genuine ones. Recently, the arbiter PUF (APUF) and its variants are intensively researched aiming at using for a secure authentication system. However, vulnerability of APUFs against machine-learning attacks was reported. Upon the situation, the double arbiter PUF (DAPUF), which has a tolerance against support vector machine (SVM)-based machine-learning attacks, was proposed as another variant of APUF in 2014. In this paper, we perform a security evaluation for authentication systems using APUF and its variants against Deep-learning (DL)-based attacks. DL has attracted attention as a machine-learning method that produces better results than SVM in various research fields. Based on the experimental results, we show that these DAPUFs could be used as a core primitive in a secure authentication system if setting an appropriate threshold to distinguish a legitimate IC tags from fake ones.
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Notes
- 1.
Preliminary evaluation results on APUF, 2-1 DAPUF, and 3-1 DAPUF using DL is reported in [13].
- 2.
transformer_dataset descriptions at the beginning of the YAML file are not needed in the first-layer YAML code.
- 3.
SVM-light [7] is used as machine-learning tool.
- 4.
Instead of using the average value for U with several devices, the minimum value should be used in ideal for the purpose of considering the worst case for the secure-operation margin.
- 5.
In the case of one-to-one matching verification using PUF CRPs, we can set \(U=1\) since uniqueness is not related to the authentication performance.
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Appendices
The copyrights to full versions of the pseudo code and the constraint file belong to The University of Electro-Communications, Japan, and their authorship is attributed to K. Sakiyama, T. Machida, and M. Iwamoto.
Appendix A: Pseudo Verilog Code and User Constraint File of 3-1 DAPUF for Xilinx\(^{{\mathrm {\textregistered }}{}}\) Virtex-5\(^{{\mathrm {\textregistered }}{}}\) (XC5VLX30)
The copyrights to full versions of the pseudo code and the constraint file belong to The University of Electro-Communications, Japan, and their authorship is attributed to K. Sakiyama, T. Machida, and M. Iwamoto.
Appendix B: Third-Layer YAML Code for Pylearn2 [2]
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Yashiro, R., Machida, T., Iwamoto, M., Sakiyama, K. (2016). Deep-Learning-Based Security Evaluation on Authentication Systems Using Arbiter PUF and Its Variants. In: Ogawa, K., Yoshioka, K. (eds) Advances in Information and Computer Security. IWSEC 2016. Lecture Notes in Computer Science(), vol 9836. Springer, Cham. https://doi.org/10.1007/978-3-319-44524-3_16
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