Dual-Leak: Deep Unsupervised Active Learning for Cross-Device Profiled Side-Channel Leakage Analysis
Document Type
Conference Proceeding
Publication Date
5-25-2023
Department
Department of Electrical and Computer Engineering
Abstract
Deep Learning (DL)-based side-channel analysis (SCA), as a new branch of SCA attacks, poses a significant privacy and security threat to implementations of cryptographic algorithms. Despite their impacts on hardware security, existing DL-based SCA attacks have not fully leveraged the potential of DL algorithms. Therefore, previously proposed DL-based SCA attacks may not show the real capability to extract sensitive information from target designs. In this paper, we propose a novel cross-device SCA method, named Dual-Leak, that applies Deep Unsupervised Active Learning to create a DL model for breaking cryptographic implementations, even with countermeasures deployed. The experimental results on both the local dataset and publicly available dataset show that our Dual-Leak attack significantly outperforms state-of-the-art works while no labeled traces are required from victim devices (i.e., unsupervised learning). Countermeasures are also discussed to assure hardware security against new attacks.
Publication Title
Proceedings of the 2023 IEEE International Symposium on Hardware Oriented Security and Trust, HOST 2023
ISBN
9798350300628
Recommended Citation
Yu, H.,
Wang, S.,
Shan, H.,
Panoff, M.,
Lee, M.,
Yang, K.,
&
Jin, Y.
(2023).
Dual-Leak: Deep Unsupervised Active Learning for Cross-Device Profiled Side-Channel Leakage Analysis.
Proceedings of the 2023 IEEE International Symposium on Hardware Oriented Security and Trust, HOST 2023, 144-154.
http://doi.org/10.1109/HOST55118.2023.10133491
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17239