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

TL;DR

This paper introduces a novel anomaly traffic detection system for UAV networks using a CNN+Transformer model within an SDN and blockchain framework, enhancing security in emergency rescue scenarios.

Contribution

It proposes an integrated CNN+Transformer algorithm for anomaly detection in UAV traffic, combined with SDN and blockchain for improved security and manageability.

Findings

CTranATD outperforms CNN, Transformer, and LSTM in anomaly detection accuracy.

The architecture effectively detects time-series abnormal traffic in UAV networks.

Simulation results validate the proposed method's effectiveness.

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.