{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T10:59:39Z","timestamp":1778065179788,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T00:00:00Z","timestamp":1524528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.<\/jats:p>","DOI":"10.3390\/s18051306","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"1306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7545-5040","authenticated-orcid":false,"given":"Murilo","family":"Coutinho","sequence":"first","affiliation":[{"name":"Cybersecurity INCT Unit 6, Decision Technologies Laboratory\u2014LATITUDE, Electrical Engineering Department (ENE), Technology College, University of Bras\u00edlia (UnB), 70.910-900 Bras\u00edlia-DF, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6717-3374","authenticated-orcid":false,"given":"Robson","family":"De Oliveira Albuquerque","sequence":"additional","affiliation":[{"name":"Cybersecurity INCT Unit 6, Decision Technologies Laboratory\u2014LATITUDE, Electrical Engineering Department (ENE), Technology College, University of Bras\u00edlia (UnB), 70.910-900 Bras\u00edlia-DF, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5159-9517","authenticated-orcid":false,"given":"F\u00e1bio","family":"Borges","sequence":"additional","affiliation":[{"name":"National Laboratory for Scientific Computing; 25.651-075 Petr\u00f3polis-RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7573-6272","authenticated-orcid":false,"given":"Luis","family":"Garc\u00eda Villalba","sequence":"additional","affiliation":[{"name":"Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor Jos\u00e9 Garc\u00eda Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0117-8102","authenticated-orcid":false,"given":"Tai-Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Convergence Security, Sungshin Women\u2019s University, 249-1 Dongseon-Dong 3-ga, Seoul 136-742, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,24]]},"reference":[{"key":"ref_1","unstructured":"Simonyan, K., and Zisserman, A. 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