TU Graz Risk Assessment Tool Evaluates Potential Dangers of GNSS Interference Signals

Falsified or disrupted signals from navigation satellites can have devastating effects on critical infrastructure. A tool from TU Graz helps to detect, analyse and plan preventive measures.

Whether online banking, the electrical grid, telecommunications or normal navigation, many applications and areas today depend on reliable GNSS (Global Navigation Satellite System) signals to synchronise time and position data between multiple users and to verify data transmissions. In recent years, GNSS jamming attacks have increased significantly, especially in crisis areas, and have essentially impaired critical infrastructure. This potential danger will increase in the future, which is why preventive measures are necessary. The navigation working group at the Institute of Geodesy at Graz University of Technology (TU Graz) has dedicated itself to this topic and developed a risk assessment tool that enables companies and institutions to check their own infrastructure for hazards and prepare for potential interference.

Massive disruptions in crisis regions

"The topic of GNSS interference is highly explosive and has become massively more important in the wake of the war in Ukraine, the crisis in Israel and the civil war in Syria," says Philipp Berglez from the Institute of Geodesy. Navigation in particular is affected by deliberate disruptions in crisis regions. Even before the invasion of Ukraine, Russia began deliberately jamming GNSS receivers in the deployment regions to cause confusion. There are two ways to interfere with the signals. On the one hand, jamming, in which a noise-like interference signal is transmitted, making it impossible for the receiver to determine its position. On the other hand, there is spoofing, which aims to deliberately manipulate the receiver’s position information with a fake signal so that it outputs false information. A subtype of spoofing is "meaconing". Here, the interferers use a recorded signal that they send with a time delay in order to deceive the receiver.

The risk analysis tool uses specially developed machine learning models to recognise and evaluate risks. The researchers trained these with real and simulated data from various jamming and spoofing scenarios so that they learn to recognise the resulting deviations from correct signals. They collected the real data from the jamming attacks using several types of jammers in order to cover a larger bandwidth. The same applied to the data receivers, where different models and smartphones were used. The research team pursued three analytical approaches based on the quality of the available receiver. The simplest method is intended for receivers with the quality of a smartphone, where only position data can be analysed. With geodetic receivers, the exact measurement of distances and thus the analysis of the signal-to-noise ratio is possible, which is why the second method is used here. The most advanced and most accurate method requires special receiving equipment with which the digital GNSS signal can be analysed. The respective interference risk is indicated by a traffic light system, with green indicating no risk, yellow indicating a potential interference risk and red confirming a risk.

Detection with 96% accuracy

The team at the Institute of Geodesy tested the developed machine learning models with various interference attacks and achieved a detection accuracy of 96%. "The security and resilience of critical infrastructure to GNSS interference is an important issue not only in crisis areas, but also in Austria, as interference signals can be generated with relatively low hardware costs and publicly available software. With our risk assessment tool, operators can make an objective assessment of potential weaknesses and prepare suitable countermeasures. This makes it possible, for example, to prevent power or telecommunications network outages due to deliberate GNSS interference," says Philipp Berglez. The Institute of Geodesy has finalised the tool in a demonstrator that interested institutions can use to carry out a risk assessment.

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