The aviation industry's increasing reliance on GPS to facilitate navigation and air traffic monitoring opens new attack vectors with the purpose of hijacking UAVs or interfering with air safety. We propose Crowd-GPS-Sec to detect and localize GPS spoofing attacks on moving airborne targets such as UAVs or commercial airliners. Unlike previous attempts to secure GPS, Crowd-GPS-Sec neither requires any updates of the GPS infrastructure nor of the airborne GPS receivers, which are both unlikely to happen in the near future. In contrast, Crowd-GPS-Sec leverages crowdsourcing to monitor the air traffic from GPS-derived position advertisements that aircraft periodically broadcast for air traffic control purposes. Spoofing attacks are detected and localized by an independent infrastructure on the ground which continuously analyzes the contents and the times of arrival of these advertisements. We evaluate our system with real-world data from a crowdsourced air traffic monitoring sensor network and by simulations. We show that Crowd-GPS-Sec is able to globally detect GPS spoofing attacks in less than two seconds and to localize the attacker up to an accuracy of 150 meters after 15 minutes of monitoring time.

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Automatic Dependent Surveillance-Broadcast (ADS-B) has been widely adopted as the de facto standard for air-traffic surveillance. Aviation regulations require all aircraft to actively broadcast status reports containing identity, position, and movement information. However, the lack of security measures exposes ADS-B to cyberattacks by technically capable adversaries with the purpose of interfering with air safety. In this paper, we develop a non-invasive trust evaluation system to detect attacks on ADS-B-based air-traffic surveillance using real-world flight data as collected by an infrastructure of ground-based sensors. Taking advantage of the redundancy of geographically distributed sensors in a crowdsourcing manner, we implement verification tests to pursue security by wireless witnessing. At the core of our proposal is the combination of verification checks and Machine Learning (ML)-aided classification of reception patterns-such that user-collected data cross-validates the data provided by other users. Our system is non-invasive in the sense that it neither requires modifications on the deployed hardware nor the software protocols and only utilizes already available data. We demonstrate that our system can successfully detect GPS spoofing, ADS-B spoofing, and even Sybil attacks for airspaces observed by at least three benign sensors. We are further able to distinguish the type of attack, identify affected sensors, and tune our system to dynamically adapt to changing air-traffic conditions.