RESEARCH ON DIFFERENTIAL CRYPTANALYSIS BASED ON DEEP LEARNING

Abstract

In the age of pervasive connectivity, cryptography is a vital defensive measure for information security, and the security of cryptographic protection is of critical importance. Deep learning technology has recently made significant strides in areas like image classification and natural language processing, garnering considerable interest. Compared with classic cryptographic algorithms, modern block ciphers are more intricate, and the mappings between plaintext and ciphertext are less distinct, rendering the extraction of plaintext features from ciphertexts by neural networks as almost infeasible. However, the symbiosis of deep learning and traditional differential cryptanalysis holds promise for enhancing crypto-attack performance. Thus, the integration of deep learning theory and methods into the field of cryptography is becoming a significant trend in technological advancement. In this context, cryptanalysis is progressively developing in the direction of intelligence and automation, with an increasing number of researchers employing deep learning to assist in cryptanalytic tasks. This review aims to delve into the current research trends surrounding deep learning-supported differential cryptanalysis. It commences with a thorough recapitulation of differential analysis in cryptography and introduces common models in deep learning, along with their characteristics. Moreover, it encapsulates the design of differential classifiers powered by deep learning, inclusive of various optimization techniques utilized within these algorithms. The paper also posits directions for future research focus. Despite challenges, deep learning possesses vast potential in reinforcing conventional differential cryptanalysis, providing deeper insights for security analysis and response strategies, and serving as a valuable tool and perspective for the design and appraisal of future cryptographic solutions.