Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset

TL;DR

This paper introduces two methods, statistical time series analysis and deep learning, for detecting jamming attacks in UAV networks, achieving high accuracy and low computational requirements.

Contribution

It proposes a combined approach using statistical and deep learning techniques for effective jamming detection in UAV networks, with detailed simulation results.

Findings

Statistical method identified 84.38% of attacks at 30m distance.

Deep network achieved 99.99% accuracy for high-power jamming within 200m.

Combined methods offer a balance of efficiency and accuracy.

Abstract

Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices for their benefits during UAV transmissions. The vulnerability occurs due to the open nature of air-to-ground (A2G) wireless communication networks, which may enable network-wide attacks. This paper presents two strategies to identify Jammers in UAV networks. The first strategy is based on time series approaches for anomaly detection where the signal available in resource blocks are decomposed statistically to find trend, seasonality, and residues, while the second is based on newly designed deep networks. The joined technique is suitable for UAVs because the statistical model does not require heavy computation processing but is limited in generalizing possible attack's identification. On the other hand, the deep network can classify attacks accurately but requires more resources. The simulation considers the location and power of the jamming attacks and the UAV position related to the base station. The statistical method technique made it feasible to identify 84.38 % of attacks when the attacker was at 30 m from the UAV. Furthermore, the Deep network's accuracy was approximately 99.99 % for jamming powers greater than two and jammer distances less than 200 meters.