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1. Impacts of a Single GPS Spoofer on Multiple Receivers: Formal Analysis and Experimental Evaluation

   https://doi.org/10.1109/CCNC51664.2024.10454872  

   Chen, Wenxin; Dong, Yingfei; Duan, Zhenhai (January 2024, IEEE)

   As many mobile devices use Global Navigation Satellite Systems (GNSSs) to determine their locations for control, compromising such systems can result in serious consequences, as shown by existing GPS spoofing attacks. However, most such spoofing attacks focus on the effect of a single spoofer attacking a single receiver. In this paper, we investigate the impacts of a single spoofer on multiple receivers, motivated by research on attacking drone swarms. Our analysis independently shows that, using a single spoofer, multiple receivers at different locations in a spoofing area will see the same location reading. We consider the base case of spoofing four satellites and also the generic case when more satellites are involved in the spoofing attack. More importantly, we conduct real-world experiments to validate our analysis and demonstrate the potential threats to many practical applications. We use off-the-shelf SDR cards for spoofing and consumer GPS receivers for obtaining spoofed location readings. While this method can enable various attacks on mobile devices depending on GPS, it is also applicable to all existing GNSSs, because they use similar principles to determine locations. 

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2. GPS Swarm Spoofing: Theoretical Analysis and Practical Solutions

   Chen, Wenxin; Dong, Yingfei; Duan, Zhenhai (June 2025, IEEE)

   Although GPS spoofing of individual devices has been extensively examined, little systematic research on swarm spoofing has been conducted. In general, swarm missions may allow each device to navigate independently for different tasks, and it is much more complicated to build corresponding spoofing signals for such general cases. To address this issue, we formulate a general swarm spoofing method to explore the theoretical capabilities and limitations of common cases. We then propose a basic swarm spoofing model to show that, if we try to spoof each receiver precisely, we can only attack a small number of receivers (≤ 9) simultaneously in theory. However, in practice, we often need to deal with many receivers. Therefore, we develop a method that can spoof more receivers with acceptable errors. We present a method to construct spoofing messages and evaluate its effectiveness in practical settings with simulations. Although this work focuses on the GPS system, the proposed ideas can be applied to other GNSSs. 

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3. Detection of Induced GNSS Spoofing Using S-Curve-Bias

   https://doi.org/10.3390/s19040922  

   Wang, Wenyi; Li, Na; Wu, Renbiao; Closas, Pau (February 2019, Sensors)

   In Global Navigation Satellite System (GNSS), a spoofing attack consists of forged signals which possibly cause the attacked receivers to deduce a false position, a false clock, or both. In contrast to simplistic spoofing, the induced spoofing captures the victim tracking loops by gradually adjusting it’s parameters, e.g., code phase and power. Then the victims smoothly deviates from the correct position or timing. Therefore, it is more difficult to detect the induced spoofing than the simplistic one. In this paper, by utilizing the dynamic nature of such gradual adjustment process, an induced spoofing detection method is proposed based on the S-curve-bias (SCB). Firstly, SCB in the inducing process is theoretically derived. Then, in order to detect the induced spoofing, a detection metric is defined. After that, a series of experiments using the Texas spoofing test battery (TEXBAT) are performed to demonstrate the effectiveness of the proposed algorithm. 

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4. A Comparative Analysis of the Ensemble Models for Detecting GPS Spoofing attacks on UAVs

   https://doi.org/10.1109/CCWC54503.2022.9720738  

   Gasimova, Aydan; Khoei, Tala Talaei; Kaabouch, Naima (January 2022, IEEE Annual Computing and Communication Workshop and Conference)

   Unmanned Aerial Vehicles have been widely used in military and civilian areas. The positioning and return-to-home tasks of UAVs deliberately depend on Global Positioning Systems (GPS). However, the civilian GPS signals are not encrypted, which can motivate numerous cyber-attacks on UAVs, including Global Positioning System spoofing attacks. In these spoofing attacks, a malicious user transmits counterfeit GPS signals. Numerous studies have proposed techniques to detect these attacks. However, these techniques have some limitations, including low probability of detection, high probability of misdetection, and high probability of false alarm. In this paper, we investigate and compare the performances of three ensemble-based machine learning techniques, namely bagging, stacking, and boosting, in detecting GPS attacks. The evaluation metrics are the accuracy, probability of detection, probability of misdetection, probability of false alarm, memory size, processing time, and prediction time per sample. The results show that the stacking model has the best performance compared to the two other ensemble models in terms of all the considered evaluation metrics. 

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5. Wireless Attacks on Aircraft Instrument Landing Systems

   Sathaye, Harshad; Schepers, Domien; Ranganathan, Aanjhan; Noubir, Guevara (August 2019, 28th USENIX Security Symposium (USENIX Security 19))

   Modern aircraft heavily rely on several wireless technologies for communications, control, and navigation. Researchers demonstrated vulnerabilities in many aviation systems. However, the resilience of the aircraft landing systems to adversarial wireless attacks have not yet been studied in the open literature, despite their criticality and the increasing availability of low-cost software-defined radio (SDR) platforms. In this paper, we investigate the vulnerability of aircraft instrument landing systems (ILS) to wireless attacks. We show the feasibility of spoofing ILS radio signals using commercially-available SDR, causing last-minute go around decisions, and even missing the landing zone in low-visibility scenarios. We demonstrate on aviation-grade ILS receivers that it is possible to fully and in fine-grain control the course deviation indicator as displayed by the ILS receiver, in real-time. We analyze the potential of both an overshadowing attack and a lower-power single-tone attack. In order to evaluate the complete attack, we develop a tightly-controlled closed-loop ILS spoofer that adjusts the adversary's transmitted signals as a function of the aircraft GPS location, maintaining power and deviation consistent with the adversary's target position, causing an undetected off-runway landing. We systematically evaluate the performance of the attack against an FAA certified flight-simulator (X-Plane)'s AI-based autoland feature and demonstrate systematic success rate with offset touchdowns of 18 meters to over 50 meters. 

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