A Backpropagation Extreme Learning Machine Approach to Fast Training Neural Network-Based Side-Channel Attack

A Backpropagation Extreme Learning Machine Approach to Fast Training Neural Network-Based Side-Channel Attack

2021 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

10.1109/AsianHOST53231.2021.9699677

http://dx.doi.org/10.1109/asianhost53231.2021.9699677

Xuyang Huang, Ming Ming Wong, Anh Tuan Do, Wang Ling Goh

14 February 2022

Huang, X., Wong, M. M., Do, A. T., & Goh, W. L. (2021). A Backpropagation Extreme Learning Machine Approach to Fast Training Neural Network-Based Side-Channel Attack. 2021 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). https://doi.org/10.1109/asianhost53231.2021.9699677

This work presented new Deep learning Side-channel Attack (DL-SCA) models that are based on Extreme Learning Machine (ELM). Unlike the conventional iterative backpropagation method, ELM is a fast learning algorithm that computes the trainable weights within a single iteration. Two models (Ensemble bpELM and CAE-ebpELM) are designed to perform SCA on AES with Boolean masking and desynchronization/jittering. The best models for both attack tasks can be trained 27x faster than MLP and 5x faster than CNN respectively. Verified and validated using ASCAD dataset, our models successfully recover all 16 subkeys using approximately 3K traces in the worst case scenario.

Publisher Copyright

This research / project is supported by the Singapore Cybersecurity Consortium (SGCSC) - Singapore Cybersecurity Consortium (SGCSC)
Grant Reference no. : 2020-S01

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978-1-6654-4185-8

Institute of Microelectronics