A Light Boosting-based ML Model for Detecting Deceptive Jamming Attacks on UAVs
Hadjar Ould Slimane, Selma Benouadah, Tala Talaei Khoei, Naima Kaabouch
Abstract
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.