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Memristors are promising entropy sources for realizing hardware\u2010based security primitives due to their intrinsic randomness and stochastic properties. With the adoption of memristors among several technologies that meet essential requirements, the neural network physically unclonable function (NNPUF) is proposed, a novel PUF design that takes advantage of deep learning algorithms. The proposed design integrated with the memristor array can be constructed easily because the system does not depend on write operation accuracy. To contemplate a nondifferentiable module during training, an original concept of loss called PUF loss is devised. Iterations of weight update with the loss function bring about optimal NNPUF performance. It is shown that the design achieves a near\u2010ideal 50% average value for security metrics, including uniformity, diffuseness, and uniqueness. This means that the NNPUF satisfies practical quality standards for security primitives by training with PUF loss. It is also demonstrated that the NNPUF response has an unassailable resistance against deep learning\u2010based modeling attacks, which is verified by the near\u201050% prediction model accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202100111","type":"journal-article","created":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T05:57:44Z","timestamp":1633759064000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Neural Network Physically Unclonable Function: A Trainable Physically Unclonable Function System with Unassailability against Deep Learning Attacks Using Memristor Array"],"prefix":"10.1002","volume":"3","author":[{"given":"Junkyu","family":"Park","sequence":"first","affiliation":[{"name":"The School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon 34141 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoonji","family":"Lee","sequence":"additional","affiliation":[{"name":"The School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon 34141 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hakcheon","family":"Jeong","sequence":"additional","affiliation":[{"name":"The School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon 34141 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-3360","authenticated-orcid":false,"given":"Shinhyun","family":"Choi","sequence":"additional","affiliation":[{"name":"The School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST)  Daejeon 34141 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,10,7]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"G. 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