A DCA Attack Scheme and a Genetic Algorithm-based Intelligent Fault Injection Algorithm For White-Box AES Implementation

In this paper we attack the white-box AES implementation with Differential Correlation Analysis (DCA) and fault injection, which are major risks the white-box AES implementation is facing now. A differential computation analysis (DCA) attack strategy is presented targeted at the implementation of the white-box AES algorithm based on a combination T-box. Using the DCA attack method, 16 subkeys are extracted successfully, which underscores the presence of a notable side-channel security vulnerability within the white-box AES encryption scheme. In order to improve the protection ability of the scheme against side-channel attacks, the enhanced rotating S-box masking scheme is adopted. No security risk has been detected which proves the validity of the mask scheme. Moreover, this research performs the fault injection attack on white-box implementation. However, the process typically demands substantial time and resources due to its manual testing nature. To optimize the method, we introduce an intelligent fault injection algorithm utilizing a genetic algorithm, which automates the fault injection attack to white-box implementation. This proposed scheme exemplifies intelligence and automation in the testing process. The empirical comparison highlights that the automated fault injection algorithm significantly enhances attack efficiency by 980% when compared to the random parameter search method.