Paper 2024/1092

Fusion Channel Attack with POI Learning Encoder

Abstract

In order to challenge the security of cryptographic systems, Side-Channel Attacks exploit data leaks such as power consumption and electromagnetic emissions. Classic Side-Channel Attacks, which mainly focus on mono-channel data, fail to utilize the joint information of multi-channel data. However, previous studies of multi-channel attacks have often been limited in how they process and adapt to dynamic data. Furthermore, the different data types from various channels make it difficult to use them effectively. This study introduces the Fusion Channel Attack with POI Learning Encoder (FCA), which employs a set of POI Learning encoders that learn the inverse base transformation function family and project the data of each channel into a unified fusion latent space. Furthermore, our method introduces an optimal transport theory based metric for evaluating feature space fusion, which is used to assess the differences in feature spaces between channels. This model not only enhances the ability to process and interpret multi-source data, but also significantly improves the accuracy and applicability of SCAs in different environments.