Legacy and novel technology for the communication of aircraft and unmanned aerial vehicles (UAV) abound. Security issues have been shown for example in FLARM [1], TCAS [2], MLAT [3] and practically all other deployed legacy systems. Interference with GPS systems is a widespread daily occurance for any aircraft transiting wide conflict zones. [4]

Multilateration (MLAT) can determine the position of a transmitter using observations from geographically distributed receivers. It is an important independent complement to self-reported aircraft positions, but several practical challenges remain when signals and receiver infrastructure are incomplete or unreliable.

We offer two related Master thesis topics addressing these challenges using data and infrastructure from the OpenSky Network (https://opensky-network.org):

Topic A: Signal Fingerprinting for Identifier-Independent Multilateration

Many existing MLAT systems rely on explicit identifiers or other readily available information to associate observations. This thesis will investigate signal-fingerprinting techniques for recognizing related aircraft transmissions when such information is missing, unreliable, or unavailable.

The goal is to develop and evaluate methods that make a broader range of signals usable for distributed aviation monitoring and multilateration. The precise signal classes, features, and evaluation methodology will be defined together with the student at the beginning of the thesis.

Topic B: Aircraft-Signal-Based Time Synchronization and MLAT

Distributed receiver networks require sufficiently accurate timing, which can become challenging when conventional synchronization sources are degraded or unavailable. This thesis will investigate robust synchronization and multilateration techniques using OpenSky data and infrastructure.

The work will combine algorithmic research with the development of a practical prototype and an evaluation under representative conditions. The exact system architecture, synchronization approach, target signals, and operational scenarios are intentionally left open and will be discussed with the student.

Required Skills

  • Signal processing
  • Programming in Python and/or C/C++
  • Statistical data analysis and algorithm evaluation
  • Familiarity with wireless systems, distributed systems, or software-defined radios is beneficial

[1] On the Security of the FLARM Collision Warning System Boya Wang‚ Giorgio Tresoldi‚ Martin Strohmeier and Vincent Lenders In Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security. May, 2022.

[2] On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS) G Longo, M Strohmeier, E Russo, A Merlo, V Lenders 33rd USENIX Security Symposium (USENIX Security 24)

[3] Universal Spoofing of Real-World Aircraft Multilateration. Senn, O., Tresoldi, G., Moser, D., Lenders, V. and Strohmeier, M., 2025, June. In 18th ACM Conference on Security and Privacy in Wireless and Mobile Networks (pp. 268-273).

[4] Navigating Turbulence: Understanding New GNSS Risks in Conflict Zones M Felux, V Lenders, M Strohmeier, B Figuet, R Monstein 2025 17th International Conference on Cyber Conflict (CyCon)