An effective cryptanalysis of DES for secure communication using hybrid cryptanalysis and deep neural network

In today's world, information security plays a key role in data storage and communication owing to the modern evolution of digitized data interchange in electronic mode. Cryptography is a widely preferred technique for securing transmitted information by transforming the original text into cipher text. A few cryptographic techniques have the inability to protect the data which are vulnerable to a distinct class of attacks. Therefore, a reliable cryptographic technique is necessitated for enlarging information security. In this paper, a new hybrid cryptanalysis (HCA) model has been proposed to acquire optimal cryptanalysis of data encryption standard (DES). It combines linear cryptanalysis (LCA) and neural cryptanalysis (NCA) for enhancing the performance of the cryptographic system with minimal time complexity. Primarily, the HCA model employs LCA to break cryptographic codes by analyzing linear approximations of the cryptographic algorithm. NCA is then applied with the aid of a deep neural network for identifying patterns in larger datasets. These patterns can be further utilized to break encryption. The efficacy of the proposed HCA model will be assessed through the assessment of three different datasets. The results manifest that the accuracy of the proposed model is increased up to 97.66% and the time needed to break encryption can be reduced.