Transpositional Neurocryptography Using Deep Learning

Cryptanalysis (the study of methods to read encrypted information without knowledge of the encryption key) has traditionally been separated into mathematical analysis of weaknesses in cryptographic algorithms, on the one hand, and side-channel attacks which aim to exploit weaknesses in the implementation of encryption and decryption algorithms. Mathematical analysis generally makes assumptions about the algorithm with the aim of reconstructing the key relating plain text to cipher text through brute-force methods. Complexity issues tend to dominate the systematic search for keys. To date, there has been very little research on a third cryptanalysis method: learning the key through convergence based on associations between plain text and cipher text. Recent advances in deep learning using multi-layered artificial neural networks (ANNs) provide an opportunity to reassess the role of deep learning architectures in next generation cryptanalysis methods based on neurocryptography (NC). In this paper, we explore the capability of deep ANNs to decrypt encrypted messages with minimum knowledge of the algorithm. From the experimental results, it can be concluded that DNNs can encrypt and decrypt to levels of accuracy that are not 100% because of the stochastic aspects of ANNs. This aspect may however be useful if communication is under cryptanalysis attack, since the attacker will not know for certain that key K used for encryption and decryption has been found. Also, uncertainty concerning the architecture used for encryption and decryption adds another layer of uncertainty that has no counterpart in traditional cryptanalysis.