基于知识蒸馏的无人机群联邦轻量级入侵检测
Federated Lightweight Intrusion Detection in Drone Swarms with Knowledge Distillation
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中文总结 AI 辅助
针对无人机群网络易受网络威胁且传统方法有局限的问题，提出基于轻量级联邦学习的入侵检测系统，用知识蒸馏增强深度神经网络，降低模型复杂度与通信成本，实验显示该方法检测准确率高，能有效降低通信和计算开销。
中文摘要 AI 辅助
无人机群越来越多地应用于监视、灾难响应和基础设施监测等关键应用中。然而，它们对开放通信渠道的依赖以及有限的计算资源使其容易受到各种网络威胁。专门为无人机环境和操作设计的入侵检测系统（IDS）的兴趣日益浓厚。传统的基于机器学习的方法需要从群中的异构无人机收集所有数据并在中央服务器上进行处理，这可能并不总是可行的。联邦学习（FL）作为一种有前途的分布式解决方案出现，具有额外的隐私保护功能。尽管存在潜在的研究，但传统的基于FL的IDS框架仍然面临通信和计算开销挑战，在实际资源约束下实现效率和有效检测之间的平衡仍然是一个挑战。因此，我们提出了一种基于轻量级FL的IDS，专为无人机群网络量身定制，使用通过知识蒸馏（KD）增强的深度神经网络（DNN）来降低模型复杂性和通信成本，同时不牺牲检测性能。我们使用Raspberry Pi 4设备和真实世界的无人机网络数据集评估我们的框架。我们的方法展示了约98.6%的检测准确率，同时将总体通信成本降低了约70%，计算开销降低了29%。这些结果表明，FL与KD相结合是在资源受限的无人机网络中进行安全高效部署的实用且合适的解决方案。
英文摘要
Drone swarms are increasingly deployed in critical applications such as surveillance, disaster response, and infrastructure monitoring. However, their reliance on open communication channels and their limited computational resources make them vulnerable to a wide range of cyber-threats. There is a growing interest in intrusion detection systems (IDS) specifically designed for drone environments and operations. However, the conventional solutions including Machine Learning (ML)-based approaches require collecting all data from heterogeneous drones in the swarm and processing on a central server may not be always feasible. Federated Learning (FL) has emerged as a promising distributed solution with an additional privacy-preserving feature. Even though potential studies exist, conventional FL-based IDS frameworks still face communication and computational overhead challenges, while achieving a balance between efficiency and effective detection under practical resource constraints remains a challenge. Therefore, we propose a lightweight FL-based IDS tailored for drone swarm networks using deep neural networks (DNN) enhanced with knowledge distillation (KD) to reduce model complexity and communication costs without sacrificing detection performance. We evaluate our framework using Raspberry Pi 4 devices and a real-world drone network dataset. Our approach demonstrates a detection accuracy of approximately 98.6% while reducing overall communication cost by around 70% and computational overhead by 29%. These results show that FL combined with KD is a practical and suitable solution for secure and efficient deployment in resource-constrained drone networks.
