Autoencoder Assist: An Efficient Profiling Attack on High-Dimensional Datasets

Deep learning (DL)-based profiled attack has been proved to be a powerful tool in side-channel analysis. However, most attacks merely focus on small datasets, in which their points of interest are well-trimmed for attacks. Countermeasures applied in embedded systems always result in high-dimensional side-channel traces, i.e., the high-dimension of each input trace. These traces inevitably require complicated designs of neural networks and large sizes of trainable parameters for exploiting the correct keys. Therefore, performing profiled attacks (directly) on high-dimensional datasets is difficult. To bridge this gap, we propose a dimension reduction tool for high-dimensional traces by combining signal-to-noise ratio (SNR) analysis and autoencoder. With the designed asymmetric undercomplete autoencoder (UAE) architecture, we extract a small group of critical features from numerous time samples. The compression rate by using our UAE method reaches 40x on synchronized datasets and 30x on desynchronized datasets. This preprocessing step facilitates the profiled attacks by extracting potential leakage features. To demonstrate its effectiveness, we evaluate our proposed method on the raw ASCAD dataset with 100,000 samples in each trace. We also derive desynchronized datasets from the raw ASCAD dataset and validate our method under random delay effect. We further propose a 2 ⁿ -structure MLP network as the attack model. By applying UAE and attack model on these traces, experimental results show all correct subkeys on synchronized datasets and desynchronized datasets are successfully revealed within hundreds of seconds. This indicates that our autoencoder can significantly facilitate DL-based profiled attacks on high-dimensional datasets.