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

arXiv: https://arxiv.org/abs/2203.11373 · HTML: https://mlsanity.com/paper/2203.11373/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication](https://mlsanity.com/paper/2003.07308/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference](https://mlsanity.com/paper/2207.10810/) (2022)

## Abstract (arXiv)

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.