Denoising low SNR electromagnetic conducted emissions using an improved DnCNN based mode

Abstract

Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data.