基于粒子群优化多核支持向量数据描述的广播式自动相关监视异常数据检测模型

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

广播式自动相关监视(ADS-B)作为新一代空中交通管理(ATM)通信协议,是未来空管监视系统的关键技术。目前,由于ADS-B采用明文格式广播发送数据,其安全性问题受到挑战。针对ADS-B易受到的欺骗干扰,该文将ADS-B位置数据和同步的二次雷达(SSR)数据作差,将两者的差值作为样本数据。利用多核支持向量数据描述(MKSVDD)训练样本,得到了超球体分类器,此超球体分类器能检测出ADS-B测试样本中的异常数据。并且,通过粒子群算法(PSO)优化了GaussLapl和GaussTanh两种MKSVDD的惩罚因子、多核核函数系数以及核参数,提高了异常数据检测性能。实验结果表明,对于随机位置偏移、固定位置偏移、拒绝服务(DOS)攻击和重放攻击,粒子群优化多核支持向量数据描述(PSO-MKSVDD)模型能检测出这4种攻击类型的异常数据。且相较于其他机器学习和深度学习方法,该模型的适应性更好,异常检测的召回率和检测率更优。证明该模型可用于ADSB异常数据的检测。

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.