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Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography

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
2
National Laboratory for Scientific Computing; 25.651-075 Petrópolis-RJ, Brazil
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
4
Department of Convergence Security, Sungshin Women’s University, 249-1 Dongseon-Dong 3-ga, Seoul 136-742, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(5), 1306; https://doi.org/10.3390/s18051306
Received: 25 March 2018 / Revised: 13 April 2018 / Accepted: 18 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Abstract

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. View Full-Text
Keywords: Adversarial Neural Cryptography; Artificial Intelligence; Chosen-Plaintext Attack; Cryptography; Neural Network; One-Time Pad Adversarial Neural Cryptography; Artificial Intelligence; Chosen-Plaintext Attack; Cryptography; Neural Network; One-Time Pad
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Coutinho, M.; De Oliveira Albuquerque, R.; Borges, F.; García Villalba, L.J.; Kim, T.-H. Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography. Sensors 2018, 18, 1306.

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