Leveraging deep cnn and transfer learning for side-channel attack
Proceedings - International Symposium on Quality Electronic Design, ISQED
The adoption of deep neural networks for profiled side-channel attacks provides powerful options for leakage detection and key retrieval of secure products. Although deep learning is being widely adopted for computer vision, less research has been prominent in template-based profiling power SCA attacks. In addition, most of the existing works fall into a one-dimensional (1-D) CNN technique rather than two-dimensional (2-D) CNN methods. Training a deep 2-D CNN from scratch is computationally expensive and requires a large amount of training data. To overcome these challenges, we adopt deep 2-D CNNs, GoogLeNet, InceptionV3, VGG16, and MobileNetV2 pre-trained to identify all possible AES key bytes. In order to use 2-D CNN and transfer learning, we also propose a novel multi-scale continuous wavelet transform of the power traces and generate scalograms from the wavelet coefficients. Moreover, we propose ResNet 1-D CNN architecture using power traces signal to break AES-128 implementation. To evaluate our proposed work, a key rank metric with the ASCAD dataset is utilized. Our proposed deep CNN framework achieves $\geq 99$ accuracy when key rank is less than 10.
Amit Garg and Nima Karimian. "Leveraging deep cnn and transfer learning for side-channel attack" Proceedings - International Symposium on Quality Electronic Design, ISQED (2021): 91-96. https://doi.org/10.1109/ISQED51717.2021.9424305