# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 1 Introduction  
With the fast development of the Internet of Things (IoT) and cyber-physical systems (CPSs), many fields (like healthcare [1], agriculture [2], and transportation [3]) have had significant improvements. With the massive deployment of artificial intelligence (AI) and machine learning (ML), 5G New Radio (5G NR)-enabled IoT and CPSs have been implemented in every aspect of human life [4], providing much more accurate, reliable, and intelligent assistance to human activities than a few decades ago. The assistance also helps humans explore unknown areas with much greater assurance of security and safety. At the same time, the new transportation evolution connects IoT and CPS in ground, aerial, and space aspects. Unmanned aerial vehicles (UAVs), as a principal part of IoT and CPSs, connect ground devices with space systems via aerial platforms that are critical to space-aerial-ground communication (SAGC) [5]. Due to their flexibility, UAVs can provide instant services to disaster areas, help workers inspect power lines [6], and assist farmers in spraying pesticides on plants [7]. Accurate localization helps a UAV detect its positioning and control its precise positions,so that we can ensure that the UAV finishes tasks with high accuracy. The Global Position System (GPS) module leverages satellites' signals to calculate the distance to each satellite so that GPS can output its positions. The GPS is dependent on the accuracy and strength of the satellite's signal, so the GPS utilizes the strong signal to calculate the distance and then outputs its position. Based on this mechanism, hackers can simulate fake GPS signals and amplify them to be over the satellite's signal [8] so the GPS receives the fake GPS signals. The fake GPS signals lead to incorrect localization generation for the GPS module. If the hackers attack the GPS installed in a UAV, they can use the wrong localization to control a UAV to fly into sensitive areas, which can threaten public safety and private security. The conventional approaches leverage signal processing with equipment to detect abnormal signal sources so that fake GPS signals can be identified. The conventional approaches can identify fake GPS signals with high accuracy, which is good for ground devices [9]. However, the approaches need heavy equipment installed in a UAV, which is not feasible for deployment on a large scale. There are some novel approaches that leverage deep learning and machine learning [10] to detect abnormal GPS signals and identify GPS spoofing. However, the previous methods need a lot of power for computation, which is not suitable for compact UAVs [11]. The conventional approaches cannot provide a compact approach to help UAVs identify GPS spoofing during flight. Without correct identification of GPS spoofing, UAVs cannot prevent fake GPS signals, so the UAV may be compromised and used to conduct crimes by hackers. In this paper, we propose a novel approach to implementing a lightweight detection algorithm in a small UAV system to detect GPS spoofing. GPS localization is time-sequential. To achieve a good prediction of GPS localization, the algorithm is supposed to have the capability of time sequence prediction. Different from the conventional window-based method, the novel approach detects point-by-point and saves a lot of time on spoofing detection. With long-short term memory (LSTM), we leverage knowledge distillation (KD) to have a lightweight LSTM-enabled detection algorithm that can be deployed in a UAV and assist UAVs in identifying GPS spoofing. The organization of this paper is as follows: Section 1 introduces our motivations and contributions. Section 2 presents the related work of GPS spoofing and knowledge distillation. Section 3 introduces the system modeling for GPS spoofing. Section 4 states the problems we solved in the paper. Section 5 presents our methodology solving for the problems. Section 6 demonstrates the performance evaluation for our approaches. Section 7 concludes the paper and gives some insights into future work.