用CNN+Transformer检测无人机应急网络异常流量，提升安全防护能力
CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for Emergency Rescue
- 结合CNN与Transformer构建时序异常检测模型
- 在仿真中优于单独的CNN、Transformer和LSTM模型
- 适合关注无人机网络安全的科研与工程人员
近年来，无人机（UAV）网络因其广泛应用而受到广泛关注。然而，在高异构环境下，无人机易受网络攻击，导致交通安全隐患，威胁公共安全。为此，本文提出一种基于软件定义网络（SDN）和区块链技术的新型无人机网络异常流量检测架构。其中，SDN通过分离控制面与数据面提升网络可管理性与安全性；区块链则实现去中心化身份认证与数据安全记录。此外，为有效检测基于时间序列的异常流量，本文设计了一种融合卷积神经网络（CNN）与Transformer的集成算法，命名为CTranATD。仿真结果表明，所提出的CTranATD算法在检测异常流量方面表现优异，显著优于独立的CNN、Transformer及LSTM模型。
原文摘要 · Abstract (English)
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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