With the development of technology and the decreasing of manufacturing costs, unmanned aerial vehicle (UAV) is considered to be one of the most effective relay to expand the communication coverage and improve the performance of cellular networks. However, the communication system of UAV is very susceptible to Global Positioning System (GPS) spoofing, causing it to deviate from the original trajectory and perform abnormal behavior. To address this issue, the abnormal behavior detection scheme of UAV using Recurrent Neural Networks (RNNs) is proposed in this paper. Specifically, the reliable normal behavior models for two different scenarios are established by applying RNNs to avoid the confusion of slight offset and abnormal behavior, so as to improve the accuracy of proposed detection scheme of UAV. Besides, in order to ensure the accuracy of training samples of RNNs, Direction of Arrival (DOA) estimation algorithm is used to obtain a large number of current 2D arrival angle of UAV. Moreover, an appropriate threshold is selected through amounts of experiments to measure the Normalized Root Mean Square Error (NRMSE) between the real position and the position provided by normal behavior models, thus detecting the abnormal behavior of UAV. Experimental results reveal that the proposed abnormal behavior detection scheme is of high accuracy. INDEX TERMS UAV, abnormal behavior detection scheme, RNNs, GPS spoofing. I. INTRODUCTION Facing with the rapidly growing demand for high transmission rate and high communication coverage for wireless communication services, unmanned aerial vehicle (UAV) communication has recently become an active research area [1]. Since UAV can be deployed quickly in the air because of the specific maneuverability, it can not only provide the wireless service for some hotspots, but also offer signals to regional users instead of base station (BS) when terrestrial BS fails [2]. In addition, the flexible location of UAV can supply additional performance compared with fixed infrastructure based communications. Therefore, UAV is widely used as a communication relay to extend the communication range, thereby improving the performance of cellular network and satellite communication system [3]. However, these advantages of UAV also suffer from some challenges. UAV needs a reliable navigation system during the air communication, and the most common method to The associate editor coordinating the review of this article and approving it for publication was Shui Yu.

Gas-insulated switchgear (GIS) is widely used across multiple electric stages and different power grid levels. However, the threat from several inevitable faults in the GIS system surrounds us for the safety of electricity use. In order to improve the evaluation ability of GIS system safety, we propose an efficient strategy by using machine learning to conduct SF6 decomposed components analysis (DCA) for further diagnosing discharge fault types in GIS. Note that the empirical probability function of different faults fitted by the Arrhenius chemical reaction model has been investigated into the robust feature engineering for machine learning based GIS diagnosing model. Six machine learning algorithms were used to establish models for the severity of discharge fault and main insulation defects, where identification algorithms were trained by learning the collection dataset composing the concentration of the different gas types (SO2, SOF2, SO2F2, CF4, and CO2, etc.) in the system and their ratios. Notably, multiple discharge fault types coexisting in GIS can be effectively identified based on a probability model. This work would provide a great insight into the development of evaluation and optimization on solving discharge fault in GIS.

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The new radio technology for the fifth-generation wireless system has been extensively studied all over the world. Specifically, the air interface protocols for 5G radio access network will be standardized by the 3GPP in the coming years. In the next-generation 5G new radio (NR) networks, millimeter wave (mmWave) communications will definitely play a critical role, as new NR air interface (AI) is up to 100 GHz just like mmWave. The rapid growth of mmWave systems poses a variety of challenges in physical layer (PHY) security. This paper investigates those challenges in the context of several 5G new radio communication technologies, including multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA). In particular, we introduce a ray-tracing (RT) based 5G NR network channel model and reveal that the secrecy capacity in mmWave band widely depends on the richness of radio frequency (RF) environment through numerical experiments.