@inproceedings{6e11033f6e944b9db247e57f16455f06,
title = "Does Sophisticating Double Arbiter PUF Design Ensure its Security? Performance and Security Assessments on 5-1 DAPUF",
abstract = "Double Arbiter PUFs (DAPUFs) were developed as a variant to XOR PUFs to improve resilience against machine learning attacks. A recent study on DAPUFs of sizes up to 4-1 DAPUFs showed that all examined DAPUFs were vulnerable to machine learning attacks when attackers have access to a large number of challenge-response pairs (CRPs) [1], [10]. In this paper, we implemented the 5-1 DAPUF on field programmable gate arrays (FPGAs), larger than all previously implemented DAPUFs, and carried out performance evaluations of 5-1 DAPUFs on various properties including response randomness, uniqueness, stability, and security vulnerability. Experimental study on 5-1 DAPUFs shows that responses from the same 5-1 DAPUF circuit to different challenges are adequately highly distinguishable from each other while responses generated on different devices to the same challenges are different enough. 5-1 DAPUF also records the highest randomness among all tested sizes of DAPUFs. However, the stability issue is exacerbated in 5-1 DAPUF, a drawback that is also revealed in earlier studies of DAPUFs. Machine learning attack experiments show that 5-1 DAPUF is more resilient than other DAPUFs, but its responses could still be modeled when an attacker is able to accumulate a large number of CRPs.",
keywords = "Authentication, Double Arbiter PUF, FPGA, Hardware Security, Internet of Things, Machine Learning, Physical Unclonable Functions",
author = "Alamro, {Meznah A.} and Mursi, {Khalid T.} and Yu Zhuang and Alkatheiri, {Mohammed Saeed} and Aseeri, {Ahmad O.}",
note = "Funding Information: VII. ACKNOWLEDGMENT The research was supported in part by the National Science Foundation under Grant No. CNS-1526055, and the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia through project number 383. Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9378194",
language = "English",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1788--1795",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
}