用自编码器与TSMamba结合，高效识别恶意无人机。
Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration
- 用四层三向空间Mamba结构捕捉复杂空间关系
- 二分类与多分类中召回率最高达99.8%
- 计算量更低，适合大规模部署
恶意无人机对下一代网络（NGNs）构成重大威胁，可能用于非法监控、数据窃取或投递危险物品。本文提出一种基于自编码器（AE）的集成分类系统来检测恶意无人机。所提AE采用四层三向空间Mamba（TSMamba）架构，有效捕捉识别恶意无人机行为所需的关键空间关系。第一阶段通过AE生成残差值，随后由基于ResNet的分类器处理这些残差值，实现更低复杂度与更高准确率。实验表明，在二分类和多分类场景中均有显著提升，召回率最高达99.8%，优于基准方法的96.7%。此外，该方法降低计算复杂度，更适用于大规模部署。结果表明该方法具备强鲁棒性与可扩展性，为NGN环境下的恶意无人机检测提供有效解决方案。
原文摘要 · Abstract (English)
Malicious Unmanned Aerial Vehicles (UAVs) present a significant threat to next-generation networks (NGNs), posing risks such as unauthorized surveillance, data theft, and the delivery of hazardous materials. This paper proposes an integrated (AE)-classifier system to detect malicious UAVs. The proposed AE, based on a 4-layer Tri-orientated Spatial Mamba (TSMamba) architecture, effectively captures complex spatial relationships crucial for identifying malicious UAV activities. The first phase involves generating residual values through the AE, which are subsequently processed by a ResNet-based classifier. This classifier leverages the residual values to achieve lower complexity and higher accuracy. Our experiments demonstrate significant improvements in both binary and multi-class classification scenarios, achieving up to 99.8 % recall compared to 96.7 % in the benchmark. Additionally, our method reduces computational complexity, making it more suitable for large-scale deployment. These results highlight the robustness and scalability of our approach, offering an effective solution for malicious UAV detection in NGN environments.
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