Cryptanalysis

This paper explores the cryptanalysis of the ASCON algorithm, a lightweight cryptographic method designed for applications like the Internet of Things (IoT). We utilize deep learning techniques to identify potential vulnerabilities within ASCON's structure. First, we provide an overview of how ASCON operates, including key generation and encryption processes. We generate a dataset of encrypted samples and apply various deep learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to detect weaknesses in the encryption.A detailed bitwise accuracy analysis revealed that the models' predictions for each bit hovered around 50%, indicating a performance level akin to random guessing. Specifically, Bit 1 and Bit 7 achieved slightly above 50.03% accuracy, while Bit 4 reached 50.04%. The remaining bits scored below 50%, confirming the challenges in effectively breaking ASCON's encryption. This research highlights the effectiveness of ASCON in resisting cryptanalysis and the limitations faced by machine learning methods in this context.