New approach for Jamming and Spoofing detection mechanisms for High Accuracy solutions

It is well known that GNSS high accuracy solutions are increasingly vulnerable to jamming and spoofing attacks, posing significant challenges to their reliability, security, and accuracy. In the past years, GNSS communities have witnessed an increase in the frequency and sophistication of these attacks, driven, among other factors, by the widespread availability of low-cost, off-the-shelf equipment capable of denying or even totally mislead GNSS based positioning systems.

On one hand, jamming attacks aim at inhibiting signal reception by introducing high-power noise or interference, leading to degraded performance or complete failure in determining position. Jamming detection mechanisms need to be traced to GNSS receiver mitigation measures at signal processing level, to analyze the radio frequency (RF) environment or receiver behavior. Signal-to-noise ratio (SNR) monitoring, power spectrum analysis, and signal power monitoring are commonly used to detect anomalies in signal characteristics. Jamming is often indicated with the presence of a combination of one or more dedicated indicators, opening a space to characterize different levels of jamming attack allowing to optimize a response at user level.

On the other hand, detecting spoofing attacks requires different advanced techniques to identify anomalies in satellite signals, receiver behavior, or consistency of computed position data. Indicators regarding internal consistency checks, as well as unexpected evolutions of GNSS signals typically are suspicious behaviors to be analyzed as possible attacks. Additionally, ensuring trust in the received navigation information by including cryptographic authentication mechanisms is key to quickly detect some kinds of spoofing.

This paper presents the latest enhancements on Jamming and Spoofing detection and mitigation mechanisms for GMV GSharp high accuracy and safe positioning solution. New logic allows to distinguish between different levels of attack and adapt the reactions to reduce as much as possible the impact the final user. Additionally, test results obtained from real GNSS attacks datasets will be shown.