RT Journal Article
T1 Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography
A1 Coutinho, Murilo
A1 de Oliveira Albuquerque, Robson
A1 Borges, Fábio
A1 García Villalba, Luis Javier
A1 Kim, Tai-Hoon
AB 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.
PB MDPI
SN 1424-8220
YR 2018
FD 2018-04-24
LK https://hdl.handle.net/20.500.14352/12693
UL https://hdl.handle.net/20.500.14352/12693
LA eng
NO Unión Europea. Horizonte 2020
DS Docta Complutense
RD 7 abr 2025