Key Technology of Communication Equipment Fingerprint Recognition Based on Intelligent Feature Extraction Algorithm

Abstract

The rapid development of communication and computer has brought many application scenarios to the fingerprint identification technology of communication equipment. The technology is of great significance in electronic countermeasures, wireless network security, and other fields and has been widely studied in recent years. The fingerprint identification technology of communication equipment is mainly based on the fingerprint characteristics represented on the transmitted signals of the equipment, which are different from other devices, and the connection between the characteristics and the hardware equipment is established, so as to realize the purpose of identifying the communication equipment. In this paper, the author studies the key technologies related to fingerprint recognition of communication equipment, including signal acquisition, signal feature extraction, and classifier design, and transient signal recognition equipment. In this paper, the integrated learning and deep learning based on fingerprint recognition are taken as the main research contents of communication equipment, and the fingerprint recognition scheme of communication equipment is given; the proposed scheme is verified by the measured data. Aiming at the transient signal of communication equipment, an algorithm using the short-term periodicity of signal is presented. The feature extraction of steady-state signal is realized. The autoencoder feature and four kinds of integral bispectrum feature are analyzed and visualized. Research on communication equipment individual recognition technology is based on ensemble learning. An individual recognition scheme for communication devices based on Extreme Gradient Boosting (XGBoost) classification model is studied. The Gradient Boosting Decision Tree (GBDT) model with different parameters was used as the primary learner of stacking classifier. The steady-state signal recognition of mobile phones based on deep learning is studied. The results show that the stacking recognition rate improved by about 2% compared with GBDT using multiple GBDT models with different parameters as the primary learner.