Deep Time-Frequency Denoising Transform Defense for Spectrum Monitoring in Integrated Networks

Abstract

The Space-Air-Ground-Sea Integrated Networks (SAGSIN) significantly enhance global communication by merging satellite, aviation, terrestrial, and marine networks. Crucial to SAGSIN's functionality and security is spectrum monitoring using deep learning-based Automatic Modulation Classification (AMC), essential for processing and classifying complex modulation signals. However, these AMC models are susceptible to adversarial attacks. Thus, we introduce the Deep Time-Frequency Denoising Transformation (DTFDT) defense method to mitigate the impact of adversarial attacks. The DTFDT method is comprised of a deep denoising module and a transformation module. The denoising module maps signals into the time-frequency domain, amplifying the differences between benign and adversarial examples, aiding in the elimination of adversarial perturbations. Concurrently, the transformation module develops a learnable network, generating example-specific transformation matrices suited for signal data, which diminishes the effectiveness of attacks. Extensive evaluations on two datasets, RML2016.10a and DMRadio09.real, demonstrate the superior defense capabilities of DTFDT against various attacks.