基于多尺度特征融合的功耗建模分析方法OA北大核心CSTPCD

In the wave of digitization,the rapid development of 5G and 6G technologies is leading the mobile communication systems into a new era.Advanced hardware devices and encryption chips offer robust support for the escalating demand in data processing and the growing emphasis on security.In this context,various hardware devices equipped with modern cryptographic technology are gradually evolving into indispensable cornerstones of our daily lives.These devices have the capability to resist traditional cryptographic analysis.In recent years,one of the focuses of academic research is the analysis of physical leakage occurring during the ac-tual operation of devices,a field known as Side-Channel Analysis(SCA).Deep learning-driven side-channel analysis has been widely recognized as an effective method.Aiming at the current neural network model's problems such as high demand for the number of traces,poor robustness,and slow convergence speed,this pa-per proposes a multiscale feature fusion side-channel analysis method based on CNNbest.Firstly,the structure of the feature extraction network is revised to mitigate the issue of deep feature vectors being susceptible to ex-cessive interpretation of noise details and model overfitting.Subsequently,a filtering array is used to perform Discrete Wavelet Transform(DWT)analysis,constructing multi-resolution time-frequency representations to enhance data quality.Finally,a lightweight Convolutional Block Attention Module(CBAM)incorporating channel spatial attention is introduced to improve the learning efficiency of key features in power consumption curves.Experimental results demonstrate that the proposed method reduces the power consumption curves re-quired for side-channel analysis by 88.27%compared to the original model,significantly improving analysis performance and meeting the requirements of side-channel modeling and analysis.