Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography.

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作者： Murilo,Coutinho ^[1] ; Robson,de Oliveira Albuquerque ^[2] ; Fábio,Borges ^[3] ; Luis Javier,García Villalba ^[4] ; Tai-Hoon,Kim ^[5]

作者单位： Cybersecurity INCT Unit 6, Decision Technologies Laboratory-LATITUDE, Electrical Engineering Department (ENE), Technology College, University of Brasília (UnB), 70.910-900 Brasília-DF, Brazil. murilo.coutinho@redes.unb.br. ^[1] Cybersecurity INCT Unit 6, Decision Technologies Laboratory-LATITUDE, Electrical Engineering Department (ENE), Technology College, University of Brasília (UnB), 70.910-900 Brasília-DF, Brazil. robson@redes.unb.br. ^[2] National Laboratory for Scientific Computing; 25.651-075 Petrópolis-RJ, Brazil. borges@lncc.br. ^[3] 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 José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain. javiergv@fdi.ucm.es. ^[4] Department of Convergence Security, Sungshin Women's University, 249-1 Dongseon-Dong 3-ga, Seoul 136-742, Korea. taihoonn@daum.net. ^[5]

关键词 Adversarial Neural Cryptography Chosen-Plaintext Attack One-Time Pad artificial intelligence cryptography neural network

主题词算法(Algorithms);人工智能(Artificial Intelligence);信息交流(Communication)

DOI 10.3390/s18051306

PMID 29695066

发布时间 2024-03-26

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