ADS-B Anomalous Data Detection Model Based on PSO-MKSVDD

As a new generation of Air Traffic Management(ATM) communication protocol, Automatic Dependent Surveillance-Broadcast(ADS-B) is the key technology of ATM monitoring system in the future. At present, the security of ADS-B is challenged because it broadcasts data in plaintext format. Because ADS-B is susceptible to spoofing, the difference between ADS-B position data and synchronous Secondary Surveillance Radar(SSR) data is taken as sample data. Using Multi-Kernel Support Vector Data Description(MKSVDD) to train samples, a hypersphere classifier is obtained, which can detect anomalous data in ADS-B test samples. In addition, Particle Swarm Optimization (PSO) is used to optimize GaussLapl and GaussTanh MKSVDD penalty factors, coefficients of multi-kernel functions and kernel parameters.The performance of anomaly detection is improved. Experimental results show that PSO-MKSVDD can detect anomalous data of random position deviation, fixed position deviation, Denial Of Service(DOS) attack and replay attack. In addition, compared with other machine learning and deep learning methods, this model has better adaptability and better recall rate and detection rate of anomaly detection.It is proved that this model can be used to detect ADS-B anomalous data.