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Herein, a highly secure neuromorphic system is reported, which can be implemented using a physically unclonable function (PUF) that exploits the high entropy achieved via the stochastic switching of a poly(1,3,5\u2010trivinyl\u20101,3,5\u2010trimethyl cyclotrisiloxane) (pV3D3)\u2010based memristor. The excellent insulating property of pV3D3 enhances the stochasticity of the tunneling distance for randomly ruptured Cu filaments. The pV3D3 memristor\u2010based PUF (pV3D3\u2010PUF) achieves near\u2010ideal 50% averages for uniformity and uniqueness, excellent reliability under conditions of mechanical stress and water immersion, and reconfigurability\u2010bolstering security without additional hardware. Using stochastic in\u2010memory computing, the pV3D3\u2010PUF shows resilience to machine learning attacks. Furthermore, a cryptography protocol is demonstrated, which enables artificial intelligence service implementation without security issues for PUF\u2010integrated pV3D3 memristor\u2010based neuromorphic systems.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200177","type":"journal-article","created":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T21:34:20Z","timestamp":1664746460000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Memristor\u2010Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems"],"prefix":"10.1002","volume":"4","author":[{"given":"Jungyeop","family":"Oh","sequence":"first","affiliation":[{"name":"School of Electrical Engineering Graphene\/2D Materials Research Center Korea Advanced Institute of Science and Technology (KAIST)  291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of 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