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

• URL: http://arxiv.org/abs/2503.20355v1
• Date: Wed, 26 Mar 2025 09:27:26 GMT
• Title: CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for Emergency Rescue
• Authors: Yulu Han, Ziye Jia, Sijie He, Yu Zhang, Qihui Wu,
• Abstract summary: We propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology.<n>An integrated algorithm combining convolutional neural networks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection is developed, which is called CTranATD.
• Score: 12.074051347588963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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