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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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A Light Boosting-based ML Model for Detecting Deceptive Jamming Attacks on UAVs
Advances made in Unmanned Aircraft Vehicles (UAVs) have increased rapidly in the last decade resulting in new applications in both civil and military spheres. However, with the growth in the usage of these systems, various cybersecurity challenges arose unveiling the vulnerabilities of UAV wireless networks. Among the attacks that threaten the network's availability and reduce their performance are jamming attacks. Several approaches have been proposed to address this problem; however, most of them are not suitable for UAVs due to their reduced size, weight, and power constraints. In this paper, we propose a lightweight machine learning technique, LightGBM, to detect deceptive jamming attacks on UAV networks. The performance of this model is compared to that of three boosting and bagging-based machine learning models namely, XGBoost, Gradient Boost, and Random Forest. The results show that, although the LightGBM model has slightly lower accuracy (98.4%) than Gradient Boost (99%) and Random Forest (98.87%), it is 21 times faster and occupies two times less memory during the prediction than Gradient Boost and Random Forest.
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- Award ID(s):
- 2006674
- PAR ID:
- 10354441
- Date Published:
- Journal Name:
- IEEE Annual Computing and Communication Workshop and Conference (CCWC)
- Page Range / eLocation ID:
- 0328 to 0333
- 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/CCWC54503.2022.9720830
