Session A3: Aviation, Aeronautics, and Uncrewed Aerial Applications 1

Detecting GNSS Spoofing in a Tightly Coupled INS Using Directional Statistics
Maxime Herique, Safran Electronics and Defense and U2IS, ENSTA; Benoit Geller, U2IS, ENSTA; Loïc Davain, Safran Electronics and Defense
Location: Holiday 6 (Second Floor)
Date/Time: Thursday, Sep. 11, 11:48 a.m.

This study presents a novel theoretical framework for the detection of GNSS spoofing, addressing the growing need for secure navigation solutions in critical applications such as aeronautics. We specifically target highly challenging scenarios, including so-called “perfect spoofing,” which are defined and constrained by rigorous aeronautical standards. Unlike many existing approaches, our methodology does not rely on assumptions about the spoofing attack profile, nor does it attempt to estimate the spoofed trajectory. Instead, we classify spoofing scenarios solely to construct detector performance tables.
Our detection protocol is evaluated through extensive Monte Carlo simulations, comprising 10,000 runs for each spoofing scenario, involving straight-line trajectories under varying latitude, longitude, and heading conditions. Each simulation consists of an initial nominal phase, allowing filter convergence, followed by the introduction of a spoofing attack.
Results indicate that the proposed detector achieves excellent performance for specific amplitude thresholds, notably for velocity and acceleration steps, while maintaining a low contamination rate in favorable scenarios. Interestingly, very small spoofing amplitudes do not necessarily evade detection, contrary to intuition; there appears to be an optimal spoofing level that maximizes detection difficulty. However, performance is limited for position offset attacks affecting a single iteration, suggesting avenues for future improvement, particularly with shorter detection windows or alternative statistical tests.
In conclusion, our framework advances GNSS spoofing detection capabilities, demonstrating robustness under strict regulatory constraints, and lays the groundwork for further research, including three-dimensional contexts and post-detection mitigation strategies.