轻量级联邦学习检测系统，提升无人机编队网络隐私与安全
An Efficient Privacy-preserving Intrusion Detection Scheme for UAV Swarm Networks
- 采用联邦持续学习实现分布式训练，保护数据隐私
- 多数据集准确率超96%，最高达99.99%
- 适合资源受限的无人机编队安全防护场景
无人机在监控、救灾、农业和国防等领域广泛应用，其编队网络面临多种安全攻击，可能破坏任务执行、干扰决策并危及路径规划。入侵检测系统（IDS）对保障无人机编队网络安全至关重要。然而，传统IDS依赖资源密集型神经网络，存在延迟高、隐私泄露、性能开销大和模型漂移等问题。本文提出一种新型轻量级、基于联邦持续学习的入侵检测方案，支持跨异构无人机集群的去中心化训练，有效应对数据异质性并保障隐私。实验结果表明，该模型在UKM-IDS、UAV-IDS、TLM-UAV和Cyber-Physical数据集上分类准确率分别达到99.45%、99.99%、96.85%和98.05%，显著优于现有方法。
原文摘要 · Abstract (English)
The rapid proliferation of unmanned aerial vehicles (UAVs) and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits of swarm-based UAV networks are growing significantly, they are vulnerable to various security attacks that can jeopardize the overall mission success by degrading their performance, disrupting decision-making, and compromising the trajectory planning process. The Intrusion Detection System (IDS) plays a vital role in identifying potential security attacks to ensure the secure operation of UAV swarm networks. However, conventional IDS primarily focuses on binary classification with resource-intensive neural networks and faces challenges, including latency, privacy breaches, increased performance overhead, and model drift. This research aims to address these challenges by developing a novel lightweight and federated continuous learning-based IDS scheme. Our proposed model facilitates decentralized training across diverse UAV swarms to ensure data heterogeneity and privacy. The performance evaluation of our model demonstrates significant improvements, with classification accuracies of 99.45% on UKM-IDS, 99.99% on UAV-IDS, 96.85% on TLM-UAV dataset, and 98.05% on Cyber-Physical datasets.
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