Deep-Learning-Based Side-Channel Analysis of Block Cipher PIPO With Bitslice Implementation

Recently, as deep learning has been applied to various fields, deep-learning-based side-channel analysis (SCA) has been widely investigated. Unlike traditional SCA, it can perform well independently of the attacker's ability. In this paper, we propose deep-learning-based profiled and non-profiled SCA of PIPO, (Plug-In Plug-Out), which is a bitslice block cipher that can effectively apply a countermeasure for SCA. Our datasets were captured from three different boards (XMEGA128D4, MSP430F2618, STM32F303) running PIPO-64/128. For profiled SCA, the identity (ID) labeling method exhibited better performance than the most significant bit (MSB) and hamming weight (HW) labeling methods. That is, even if each bit of the S-Box output was distributed in the power traces by the bitslice implementation, the neural network trained well each bit of the S-Box output by itself. For non-profiled SCA, we proposed a novel labeling technique that considers bitslice characteristics. We compared our proposed labeling method to MSB and HW labeling by analyzing the three aforementioned datasets. For non-profiled SCA, the proposed labeling method was more effective than the MSB and HW labeling methods on all datasets.