The security of Unmanned Aerial System (UAS) networks is becoming crucial as their number and application in several fields are increasing every day. For navigation and positioning, the Global Navigation System (GPS) is essential as it provides an accurate location for the UAS. However, since the civilian GPS signals are open and unencrypted, attackers target them in different ways such as spoofing attacks. To address this security concern, we propose a comparison of several tree-based machine learning models, namely Random Forest, Gradient Boost, XGBoost, and LightGBM, to detect GPS spoofing attacks. In this work, the dataset was built of real GPS signals that were collected using a Software Defined Radio unit and different types of simulated GPS spoofing attacks. The results show that XGBoost has the best accuracy (95.52%) and fastest detection time (2ms), which makes this model appropriate for UAS applications.
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Instance-based Supervised Machine Learning Models for Detecting GPS Spoofing Attacks on UAS
Unmanned Aerial Systems (UAS) heavily depend on the Global Positioning System (GPS) for navigation. However, the unencrypted civilian GPS signals are subject to different types of threats, including GPS spoofing attacks. In this paper, we evaluate five instance-based learning models for GPS spoofing detection in UAS, namely K Nearest Neighbor, Radius Neighbor, Linear Support Vector Machine (SVM), C-SVM, and Nu-SVM. We used software-defined radio units to collect and extract features from satellite signals. Then, we simulated three types of GPS spoofing attacks specifically the simplistic, intermediate, and sophisticated attacks. The evaluation results show that Nu-SVM outperforms the other instance learning classifiers in terms of accuracy, probability of detection, probability of false alarm, and probability of misdetection. In addition, the model shows good computational performance regarding memory usage and processing time in the detection phase.
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- Award ID(s):
- 2006674
- PAR ID:
- 10354443
- Date Published:
- Journal Name:
- IEEE Annual Computing and Communication Workshop and Conference (CCWC)
- Page Range / eLocation ID:
- 0208 to 0214
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCWC54503.2022.9720888
