Cross-Device Deep Learning Side-Channel Attacks using Filter and Autoencoder

Side-channel Analysis (SCA) attacks are effective methods for extracting encryption keys, and with deep learning (DL) techniques, much stronger attacks have been carried out on victim devices. However, carrying out this kind of attack is much more challenging in cross-device attacks when the profiling device and target device are similar but not the same, which can cause the attack to fail. We also reached this conclusion when using only DL-SCA attack on our cross-devise (Atmega microcontroller devices). Due to different processes that lead to significant device-to-device variations, the accuracy of the attack was, on average, only 23%. In this paper, we proposed a method for a real attack on cross-devices using pre-processing methods based on a combination of DL-based Autoencoder and Gaussian low-pass filter (GLPF). According to our analysis results, the accuracy of the attack using only deep learning-based Autoencoder increased to 70% on average, and it improved up to 82% by adding the GLPF technique. The results also showed that combining DL-based autoencoder and GLPF can lead to a successful attack with a maximum of 300 power traces from the victim device.