# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
###### Abstract
This step is critical in reducing computational overhead and preventing overfitting. Then, they trained a support vector regression (SVR) model that predicts flight behavior, which was compared to healthy flights using statistical bounds to identify abnormalities. The work of [6] addressed the challenges of ensuring resilient formation control in the context of drones, where communication may be lost or compromised in adversarial environments. They proposed an approach that integrates Graph Attention Networks (GANs) with deep reinforcement learning (DRL) and leverages neighboring nodes to deceive DoS attacks. The researchers in [7] proposed a state-estimation-based method for the real-time detection and isolation of three main types of cyberattacks, namely random attacks, false data injection attacks, and DoS attacks. Their approach is scalable to any nonlinear aerial system. In addition, UAVs typically rely on the GPS as their primary navigation system, continuously tracking signals from at least four satellites to accurately determine their precise spatial position at any given time. However, in some cases, these signals are affected by the ionosphere and environmental conditions, producing signal noise or, in more serious scenarios, complete signal loss. In addition, civilian GPS signals remain unsecured and could easily be intercepted and retransmitted. Third-party actors with bad intentions can build receiver-spoofer devices using only commonly available low-cost components to deceive GPS receivers. As a result, this might cause instability in determining the real-time position of GPS assets in general, and disrupt UAVs' aerial navigation in particular. Therefore, many cybersecurity researchers showed interest in implementing effective countermeasures to deal with the danger posed by such attacks. The study presented in [8] conducted a systematic review of the literature exploring the existing research dimensions on GPS spoofing attacks in the context of UAVs and flying ad hoc networks. They investigated the existing techniques for conducting different types of GPS spoofing and jamming mechanisms, their impact, and the potential to leverage these attacks as a defense concept, such as preventing civilian UAVs from accessing forbidden areas. Additionally, they explored the existing defense strategies proposed in the literature against these attacks. Since our interest lies in security precautions and defense countermeasures against GPS spoofing attacks, we proceed in this work to follow the template established in [8] to detect, mitigate, and prevent this threat. The existing countermeasures can be divided into several categories. Onboard device techniques consist of equipping UAVs with additional sensors to assist navigation and help to detect attacks. GPS signal processing techniques investigate anomalies in the characteristics of signals, such as signal strength and angle of arrival. Cryptography methods aim at establishing secure communication links between UAV nodes, satellites, and ground control stations to protect the integrity of signals. Game theory methods also contribute to the detection of GPS spoof attacks by modeling the interactions between targets and attackers as a strategic game, ultimately reaching an equilibrium, such as in the Stackelberg game. On the other hand, the relevance recently demonstrated by machine learning in various fields, such as healthcare [9], agriculture [10], and intelligent transportation systems [11], has attracted considerable attention from cyber-security researchers [12; 13; 14]. This is due to its contribution to simplifying tasks, improving decision making, and enhancing overall quality of life, in addition to its ability to achieve high precision and automatically adapt to novel conditions. Therefore, numerous studies showed interest in designing machine learning and deep learning models to face GPS spoofing attacks threatening UAVs.