GPS Spoofing Attacks and Pilot Responses Using a Flight Simulator Environment

• URL: http://arxiv.org/abs/2509.22662v1
• Date: Mon, 01 Sep 2025 13:50:51 GMT
• Title: GPS Spoofing Attacks and Pilot Responses Using a Flight Simulator Environment
• Authors: Mathilde Durieux, Kayla D. Taylor, Laxima Niure Kandel, Deepti Gupta,
• Abstract summary: Global Positioning System (GPS) spoofing involves transmitting fake signals that mimic those from GPS satellites.<n>Since GPS satellite signals are weak, the spoofed high-power signal can easily overpower them.<n>This study provides a first step toward identifying human vulnerabilities to GPS spoofing amid the ongoing debate over GPS reliance.
• Score: 0.3805935148497361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Global Positioning System (GPS) spoofing involves transmitting fake signals that mimic those from GPS satellites, causing the GPS receivers to calculate incorrect Positioning, Navigation, and Timing (PNT) information. Recently, there has been a surge in GPS spoofing attacks targeting aircraft. Since GPS satellite signals are weak, the spoofed high-power signal can easily overpower them. These spoofed signals are often interpreted as valid by the GPS receiver, which can cause severe and cascading effects on air navigation. While much of the existing research on GPS spoofing focuses on technical aspects of detection and mitigation, human factors are often neglected, even though pilots are an integral part of aircraft operation and potentially vulnerable to deception. This research addresses this gap by conducting a detailed analysis of the behavior of student pilots when subjected to GPS spoofing using the Force Dynamics 401CR flight simulator with X-Plane 11 and a Cessna 172 equipped with Garmin G1000. Spoofing scenarios were implemented via custom scripts that altered navigational data without modifying the external visual environment. Thirty student pilots from the Embry-Riddle Aeronautical University Daytona Beach campus with diverse flying experience levels were recruited to participate in three spoofing scenarios. A pre-simulation questionnaire was distributed to measure pilot experience and confidence in GPS.Inflight decision-making during the spoofing attacks was observed, including reaction time to anomalies, visual attention to interface elements, and cognitive biases. A post-flight evaluation of workload was obtained using a modified NASA Task Load Index (TLX) method. This study provides a first step toward identifying human vulnerabilities to GPS spoofing amid the ongoing debate over GPS reliance.

Related papers

• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• GPS as a Control Signal for Image Generation [95.43433150105385]
  We show that the GPS tags contained in photo metadata provide a useful control signal for image generation.<n>We train GPS-to-image models and use them for tasks that require a fine-grained understanding of how images vary within a city.
  arXiv  Detail & Related papers  (2025-01-21T18:59:46Z)
• GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning [0.8594140167290099]
  Global Navigation Satellite Systems (GNSS) are vulnerable to malicious threats such as spoofing and jamming.<n>Recent advancements in machine learning and deep learning provide promising avenues for enhancing detection and mitigation strategies.<n>This paper addresses both spoofing and jamming by tackling real-world challenges through machine learning, deep learning, and computer vision techniques.
  arXiv  Detail & Related papers  (2025-01-04T18:14:43Z)
• GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles [1.1992484839577393]
  GPS networks are vulnerable to cyber-attacks like spoofing and jamming.<n>This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly-based intrusion detection framework.<n>To the best of our knowledge, this dataset is the first of its kind and has been publicly released for the global research community to address such security challenges.
  arXiv  Detail & Related papers  (2024-05-14T06:55:16Z)
• A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance [59.21686775951903]
  Various animals exhibit accurate navigation using environment cues. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps.
  arXiv  Detail & Related papers  (2024-02-06T13:20:56Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• AdvGPS: Adversarial GPS for Multi-Agent Perception Attack [47.59938285740803]
  This study investigates whether specific GPS signals can easily mislead the multi-agent perception system. We introduce textscAdvGPS, a method capable of generating adversarial GPS signals which are also stealthy for individual agents within the system. Our experiments on the OPV2V dataset demonstrate that these attacks substantially undermine the performance of state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-30T23:13:41Z)
• Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience [4.898754501085215]
  This study explores a stealthy slow drift GPS spoofing attack, replicating the victim AV's satellite reception pattern. The attack is designed to gradually deviate from the correct route, making real-time detection challenging. Changing the pseudo ranges confuses the AV, leading it to incorrect destinations while remaining oblivious to the manipulation.
  arXiv  Detail & Related papers  (2024-01-02T17:36:07Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Intelligent GPS Spoofing Attack Detection in Power Grids [0.7034739490820968]
  GPS is vulnerable to GPS spoofing attack (GSA) In power grids, phasor measurement units (PMUs) use GPS to build time-tagged measurements. In this paper, a neural network GPS spoofing detection (NNGSD) with employing PMU data is presented to detect GSAs.
  arXiv  Detail & Related papers  (2020-05-09T20:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.