Incorporating Cluster Analysis of Feature Vectors for Non-profiled Deep-learning-Based Side-Channel Attacks

Differential deep learning analysis (DDLA) was proposed as a side-channel attack (SCA) with deep learning techniques in non-profiled scenarios at TCHES 2019. In the proposed DDLA, the adversary sets the LSB or MSB of the intermediate value in the encryption process assumed for the key candidates as the ground-truth label and trains a deep neural network (DNN) with power traces as an input. The adversary also observes metrics such as loss and accuracy during DNN training and estimates that the key corresponding to the best-fitting DNN is correct. One of the disadvantages of DDLA is the heavy computation time for the DNN models because the number of required models is the as same as the number of key candidates, which is typically 256 in the case of AES. Furthermore, the DNN models have to be trained again if the adversary changes a ground-truth label function from LSB to other labels such as MSB or HW. We propose a new deep-learning-based SCA in a non-profiled scenario to solve these problems. Our core idea is to conduct dimensionality reduction on the leakage waveform using DNN. The adversary conducts cluster analysis using the feature vectors extracted from power traces using DNN. Only one DNN needs to be trained to reveal all key bytes. In addition, once the DNN is trained, multiple label functions can be tested without the additional cost of training DNNs. We provide two case studies of attacking against AES, including AES without SCA countermeasures and the ASCAD database. The results show that the proposed method requires fewer waveforms to reveal all key bytes than DDLA. In addition, the proposed method requires 1/75 less computation time than DDLA.