A Recognition Method for Electromagnetic Leakage Signals from Covert Channels Based on CNN and LSTM

During operation, information processing terminals such as computers inevitably generate electromagnetic radiation through legitimate covert communication channels. This radiation not only carries terminal-specific useful information, which poses a security risk of information leakage, but may also contains characteristic signals of device operation, presenting a challenge to the accurate monitoring of time-domain electromagnetic anomalies. To address such time-domain electromagnetic leakage signals, a neural network algorithm combining one-dimensional convolution and long short-term memory is proposed. By fusing spatial features and temporal correlation features, the algorithm enhances the deep representation capability of the target signals, enabling intelligent identification of covert-channel electromagnetic leakage and accurate capture of time-domain electromagnetic anomalies from terminals. Experimental results demonstrate that the proposed method achieves an information recognition accuracy of over 84.7%, which can effectively improve the capability of identifying and monitoring time-domain electromagnetic signals from information processing terminals.