用迭代迁移学习提升基于位置的热侧信道攻击效果
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning
- 对每个密钥字节依次使用已训练模型作为初始权重
- 数据不足时性能提升明显，平均成功率显著提高
- 适合资源受限环境下侧信道攻击场景
本文提出将迭代迁移学习应用于深度学习模型以增强侧信道攻击。现有方法通常为每个密钥字节单独训练模型，忽略了字节间的相关性。由于不同字节攻击模型参数可能相似，可先训练一个字节的模型，再将其作为预训练模型用于其余字节的训练，该过程可迭代进行。实验表明，当输入为热图或功耗图，模型采用多层感知机或卷积神经网络时，本方法在数据量有限条件下显著提升平均攻击性能。
原文摘要 · Abstract (English)
This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks. Currently, most of the side-channel attack methods train a model for each individual byte, without considering the correlation between bytes. However, since the models' parameters for attacking different bytes may be similar, we can leverage transfer learning, meaning that we first train the model for one of the key bytes, then use the trained model as a pretrained model for the remaining bytes. This technique can be applied iteratively, a process known as iterative transfer learning. Experimental results show that when using thermal or power consumption map images as input, and multilayer perceptron or convolutional neural network as the model, our method improves average performance, especially when the amount of data is insufficient.
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