Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154903
Title: A backpropagation extreme learning machine approach to fast training neural network-based side-channel attack
Authors: Huang, Xuyang
Keywords: Engineering::Electrical and electronic engineering::Microelectronics
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Huang, X. (2021). A backpropagation extreme learning machine approach to fast training neural network-based side-channel attack. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154903
Abstract: In recent years, many side-channel attack (SCA) based on deep learning have emerged, making it possible to break protected encryption algorithms. How- ever, since the training of deep learning is based on back-propagation, a long training time is required. In deep learning SCA, because of the encryption algorithm, it is common to train multiple models based on the number of subkeys in one time attack, so the training time is multiplied as a drawback of DL- SCA. 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 at- tack tasks can be trained 27× faster than MLP and 5× 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.
URI: https://hdl.handle.net/10356/154903
Schools: School of Electrical and Electronic Engineering 
Organisations: Institute of Microelectronics (IME), A*STAR Singapore
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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