# CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for Emergency Rescue

arXiv: https://arxiv.org/abs/2503.20355 · HTML: https://mlsanity.com/paper/2503.20355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook](https://mlsanity.com/paper/2104.01283/) (2021)
- [Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework](https://mlsanity.com/paper/2405.11281/) (2024)
- [Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach](https://mlsanity.com/paper/2308.06973/) (2023)

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

- [A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks](https://mlsanity.com/paper/2510.02711/) (2025)
- [Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework](https://mlsanity.com/paper/2604.04852/) (2026)

## Abstract (arXiv)

The unmanned aerial vehicle (UAV) network has gained significant attentions in recent years due to its various applications. However, the traffic security becomes the key threatening public safety issue in an emergency rescue system due to the increasing vulnerability of UAVs to cyber attacks in environments with high heterogeneities. Hence, in this paper, we propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology. Specifically, SDN separates the control and data plane to enhance the network manageability and security. Meanwhile, the blockchain provides decentralized identity authentication and data security records. Beisdes, a complete security architecture requires an effective mechanism to detect the time-series based abnormal traffic. Thus, an integrated algorithm combining convolutional neural networks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection is developed, which is called CTranATD. Finally, the simulation results show that the proposed CTranATD algorithm is effective and outperforms the individual CNN, Transformer, and LSTM algorithms for detecting anomaly traffic.