A Lockable Abnormal Electromagnetic Signal Joint Detection Algorithm

Abstract

With the development of computers and network technologies, network security has gradually become a global problem. Network security defenses need to be carried out not only on the Internet, but also on other communication media, such as electromagnetic signals. Existing electromagnetic signal communication is easily intercepted or infiltrated. In order to effectively detect the abnormal electromagnetic signal to find out the specific location, then classify it, it is necessary to study the way of communication. The existing electromagnetic signal detection accuracy is low and cannot be located. Considering the characteristics of different power sources in different locations, combined with spark streaming technology and machine learning classification technology, a joint platform for electromagnetic signal anomaly detection based on big data analysis is proposed. The electromagnetic signal is abnormally detected by feature comparison and small signal analysis, and the position and number between the signal sources are determined by three-point positioning and signal attenuation. The experimental results show that the method can detect abnormal electromagnetic signals and classify abnormal electromagnetic signals well, the accuracy rate can reach 95%, and the positioning accuracy can reach 89%.