Paper 2023/1292

Enhancing Data Security: A Study of Grain Cipher Encryption using Deep Learning Techniques

Abstract

Data security has become a paramount concern in the age of data driven applications, necessitating the deployment of robust encryption techniques. This paper presents an in-depth investigation into the strength and randomness of the keystream generated by the Grain cipher, a widely employed stream cipher in secure communication systems. To achieve this objective, we propose the construction of sophisticated deep learning models for keystream prediction and evaluation. The implications of this research extend to the augmentation of our comprehension of the encryption robustness offered by the Grain cipher, accomplished by harnessing the power of deep learning models for cryptanalysis. The insights garnered from this study hold significant promise for guiding the development of more resilient encryption algorithms, thereby reinforcing the security of data transmission across diverse applications.

Note: In recent years, an emerging trend in cryptographic research involves the integration of deep learning models to analyze and enhance encryption schemes. In this context, the application of deep learning techniques to predict and evaluate the keystream of stream ciphers, such as the Grain cipher, has garnered considerable attention. Utilizing powerful neural network architectures, such as Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs), researchers have endeavored to predict the Grain cipher’s keystream effectively. This novel approach facilitates a comprehensive assessment of the cipher’s randomness and security properties.