Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography

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Coutinho, Murilo and de Oliveira Albuquerque, Robson and Borges, Fábio and García Villalba, Luis Javier and Kim, Tai-Hoon (2018) Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography. Sensors, 18 (5). p. 1306. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s18051306




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.


Item Type:Article
Uncontrolled Keywords:Adversarial Neural Cryptography; Artificial Intelligence; Chosen-Plaintext Attack; Cryptography; Neural Network; One-Time Pad
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Computer science > Computer security
ID Code:67669
Deposited On:07 Sep 2021 07:32
Last Modified:07 Sep 2021 07:39

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