{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:14:24Z","timestamp":1772777664285,"version":"3.50.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>\n            With the exceeding advancement in technology, the sophistication of attacks is considerably increasing. Standard security methods fall short of achieving the security essentials of IoT against physical attacks due to the nature of IoTs being resource-constrained elements.\n            <jats:bold>Physical Unclonable Functions (PUFs)<\/jats:bold>\n            have been successfully employed as a lightweight memoryless solution to secure IoT devices. PUF is a device that exploits the integrated circuits\u2019 inherent randomness originated during the fabrication process to give each physical entity a unique identifier. Nevertheless, because PUFs are vulnerable to mathematical clonability,\n            <jats:bold>Feed-Forward Arbiter PUF (FF PUF)<\/jats:bold>\n            was introduced to withstand potential attack methods. Motivated by the necessity to expose a critical vulnerability of the standard FF PUFs design, we introduce a problem-tailored adversarial model to attack FF PUF design using a carefully engineered loop-specific neural network-based design calibrated and trained using FPGA-based in-silicon implementation data to exhibit real-world attacking scenarios posed on FF PUFs, in addition to applying simulated data. The empirical results show that the proposed adversarial model adds outperforming results to the existing studies in attacking FF PUFs, manifesting the improved efficiency in breaking FF PUFs. We demonstrate our high-performing results in numerical experiments of language modeling using the deep Neural Networks method.\n          <\/jats:p>","DOI":"10.1145\/3557742","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T12:35:47Z","timestamp":1660653347000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Problem-tailored Adversarial Deep Neural Network-Based Attack Model for Feed-Forward Physical Unclonable Functions"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-4551","authenticated-orcid":false,"given":"Ahmad O.","family":"Aseeri","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia"}]}],"member":"320","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. 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