A Machine Learning Approach for the Detection of Injection Attacks on ADS-B Messaging Systems

• Published in: 2023 International Conference on Computing, Networking and Communications (ICNC) pp. 293 - 297
• Main Authors: Price, Joshua, Slimane, Hadjar Ould, Shamaileh, Khair Al, Devabhaktuni, Vijay, Kaabouch, Naima
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.02.2023
• Subjects: Analytical models; Atmospheric modeling; Automatic dependent surveillance-broadcast (ADS-B); Computational modeling; Feature extraction; federal aviation administration (FAA); Machine learning; machine learning (ML); Measurement; message injection; national airspace system (NAS); Predictive models
• DOI: 10.1109/ICNC57223.2023.10074232

This work proposes the use of machine learning (ML) as a candidate for the detection of various types of message injection attacks against automatic dependent surveillance-broadcast (ADSB) messaging systems. Authentic ADS-B messages from a high-traffic area are collected from an open-source platform. These messages are combined with others imposing path modification, ghost aircraft injection, and velocity drift obtained from simulations. Then, ADS-B-related features are extracted from such messages and used to train different ML models for binary classification. For this purpose, authentic ADS-B data is considered as Class 1 (i.e., no attack), while the injection attacks are considered as Class 2 (i.e., presence of attack). The performance of the models is analyzed with metrics, including detection, misdetection, and false alarm rates, as well as validation accuracy, precision, recall, and Fl-score. The resulting models enable identifying the presence of injection attacks with a detection rate of 99.05%, and false alarm and misdetection rates of 0.76% and 1.10%, respectively.