Abstract
This paper proposes the first deep-learning based side-channel attacks on post-quantum key-exchange protocols. We target hardware implementations of two lattice-based key-exchange protocols—Frodo and NewHope—and analyze power side-channels of the security-critical arithmetic functions. The challenge in applying side-channel attacks stems from the single-trace nature of the protocols: each new execution will use a fresh and unique key, limiting the adversary to a single power measurement. Although such single-trace attacks are known, they have been so far constrained to sequentialized designs running on simple micro-controllers. By using deep-learning and data augmentation techniques, we extend those attacks to break parallelized hardware designs, and we quantify the attack’s limitations. Specifically, we demonstrate single-trace deep-learning based attacks that outperform traditional attacks such as horizontal differential power analysis and template attacks by up to 900% and 25%, respectively. The developed attacks can therefore break implementations that are otherwise secure, motivating active countermeasures even on parallel architectures for key-exchange protocols.
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Acknowledgements
This research is supported in part by the NSF under the Grants No. CNS 16-244770 (Center for Advanced Electronics through Machine Learning) and CNS 18-50373. NC State is an academic partner of Riscure Inc. and thanks them for providing hardware/software support for side-channel analysis. We acknowledge NVIDIA for their GPU donation and Xilinx for their FPGA donation.
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Aydin, F., Kashyap, P., Potluri, S., Franzon, P., Aysu, A. (2020). DeePar-SCA: Breaking Parallel Architectures of Lattice Cryptography via Learning Based Side-Channel Attacks. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2020. Lecture Notes in Computer Science(), vol 12471. Springer, Cham. https://doi.org/10.1007/978-3-030-60939-9_18
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