[Poster] ADS-B anomaly detection in the surveillance of low-altitude aircrafts
Keywords:Cybersecurity, ADS-B, Low-altitude air traffic, Machine learning
In the past few years, the fast increase in air traffic load has brought new challenges for air traffic controllers. The air surveillance task has become harder and as a consequence, the actual monitoring tools need to be improved. In this work, a method based on deep learning that automatically detects ADS-B spoofing attacks is proposed. As autonomous drone technologies will, in the near future, be more and more developed, this study focuses on low-altitude traffic. Our tool is based on a classifier model that raises anomalies between true aircraft trajectory shapes and supposed aircraft categories (e.g. planes, helicopters). The proposed approach can detect spoofing attacks with a success rate of 96.2%.
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Copyright (c) 2023 Melvyn PIROLLEY, Raphaël COUTURIER, Michel SALOMON, Fabrice AMBERT
This work is licensed under a Creative Commons Attribution 4.0 International License.