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Title: A Machine Learning Approach for the Detection of Injection Attacks on ADS-B Messaging Systems
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.  more » « less
Award ID(s):
2006674
PAR ID:
10440043
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
International Conference on Computing, Networking and Communications (ICNC)
Page Range / eLocation ID:
1-6
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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