Improving the Security of the LCB Block Cipher Against Deep Learning-Based Attacks

Abstract

This study presents a robust version of Light Cipher Block (LCB) by addressing the vulnerabilities identified in previous versions. The vulnerabilities in LCB, including a linear S-Box, improper bit shuffling, and subkey reusability, were thoroughly examined. To overcome these weaknesses, a modified version called Secure LCB is introduced, incorporating changes to the P-Box and key generation algorithm. Motivated by Gohr’s work at CRYPTO’19, this paper investigates the use of a neural distinguisher built upon a 1-dimensional convolutional neural network (1-d CNN). The deep learning model is tasked with identifying ciphertexts that have a specific, controlled difference in their inputs, as opposed to those with random input differences. The evaluation of the proposed Secure LCB using the neural distinguisher suggests that the modifications made to LCB have effectively enhanced its resistance against the neural distinguisher’s classification. This highlights the importance of addressing vulnerabilities in cryptographic systems and showcases the potential of machine learning techniques in cryptanalysis.

Keywords

• differential cryptanalysis
• deep learning
• LCB
• block cipher
• S-Box