LSTM-Based Cryptanalysis of Stream Cipher Espresso

The prediction of encryption keys in cryptographic systems poses a significant security threat. In this study, we investigate the feasibility of predicting the keystream used in the Espresso stream cipher through the implementation of LSTM and CNN architectures. The Espresso stream cipher, known for its lightweight design and cryptographic strength, serves as the basis for our analysis. By leveraging the capabilities of LSTM and CNN, we aim to assess the vulnerability of the Espresso stream cipher to keystream prediction attacks. By training and evaluating LSTM and CNN models on a dataset consisting of keystream bytes, we examine the extent to which the keystream used in the Espresso stream cipher can be predicted. Our experimental results demonstrate the effectiveness of LSTM and CNN in predicting the keystream bits or bytes of the Espresso stream cipher. By shedding light on the weaknesses of the Espresso stream cipher in the context of keystream prediction, this research work contributes to the field of cryptographic security.