# A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference

arXiv: https://arxiv.org/abs/2207.10810 · HTML: https://mlsanity.com/paper/2207.10810/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 16 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G](https://mlsanity.com/paper/2107.03059/) (2021)
- [Cellular Connectivity for UAVs: Network Modeling, Performance Analysis and Design Guidelines](https://mlsanity.com/paper/1804.08121/) (2018)
- [Understanding UAV Cellular Communications: From Existing Networks to Massive MIMO](https://mlsanity.com/paper/1804.08489/) (2018)
- [Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset](https://mlsanity.com/paper/2203.11373/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters](https://mlsanity.com/paper/2211.09706/) (2022)
- [Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty](https://mlsanity.com/paper/2211.02924/) (2022)

## Abstract (arXiv)

When users exchange data with Unmanned Aerial vehicles - (UAVs) over air-to-ground (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements in a city. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a deep learning (DL) approach for detecting attacks in UAVs equipped with orthogonal frequency division multiplexing (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The prospective algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks show that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. Our algorithm also detects moving attackers from a distance of 500 m.