Summary of Improving Location-based Thermal Emission Side-channel Analysis Using Iterative Transfer Learning, by Tun-chieh Lou et al.
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning
by Tun-Chieh Lou, Chung-Che Wang, Jyh-Shing Roger Jang, Henian Li, Lang Lin, Norman Chang
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed iterative transfer learning approach enhances deep learning models for side-channel attacks by leveraging similarities in parameters between different bytes. By training a model for one key byte and reusing it for others, the technique improves average performance, particularly when data is limited. The method uses thermal or power consumption map images as input and multilayer perceptron or convolutional neural network models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how machine learning can be used to improve side-channel attacks by training a model on one byte and then using it for other bytes. This helps when there’s not much data available. The approach uses special types of images as input and different kinds of models, like multilayer perceptrons or convolutional neural networks. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Neural network » Transfer learning
