基于VGGNet卷积神经网络的加密芯片模板攻击新方法

针对传统模板分析在实际攻击中的难解问题,重点研究了在图像识别领域具有优异特征提取能力的VGGNet网络模型,提出了一种基于VGGNet网络模型的模板攻击新方法。为了防止信号质量对模型准确率带来较大影响,采用相关性能量分析方法对采集到的旁路信号质量进行了检验;为了适应旁路信号数据维度特征,对网络模型结构进行适度调整;在网络训练的过程中,对梯度下降速率较慢、梯度消失、过拟合等问题进行了重点解决,并采用5折交叉验证的方法对训练好的模型进行验证。最终实验结果表明,基于VGGNet模型的测试成功率为92. 3%,较传统的模板攻击效果提升了7. 7%。

New template attack method for encryption chip based on VGGNet convolutional neural network

In order to solve the difficult problem of traditional template analysis in practical attacks, this paper focused on the VGGNet network model with excellent feature extraction capabilities in the field of image recognition. It proposed a new template attack method based on VGGNet network model. In order to prevent the signal quality from affecting the accuracy of the model, it used the correlation power analysis method to test the quality of the collected side-channel signal. In order to adapt to the dimensional characteristics of the side-channel signal data, it made modest adjustments to the network model structure. In the process of network training, it focused on issues such as slower gradient descent, gradient disappearance, and overfitting, and used a five-fold cross validation method to validate the trained model. The final experimental results show that the test success rate based on the VGGNet model is 92.3%, which is 7.7% higher than the traditional template attack effect.