用轻量联邦学习实现物联网隐私保护下的僵尸网络检测
A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
- 设备分布式训练模型，不传原始数据保隐私
- 通信高效聚合策略，通信开销显著降低
- 适合资源受限的物联网环境，检测精度高
物联网（IoT）的快速发展为创新提供了机遇，但也增加了僵尸网络驱动的网络攻击风险。传统检测方法在资源受限的IoT环境中常面临可扩展性、隐私和适应性问题。为此，本文提出一种基于联邦学习的轻量级、隐私保护型僵尸网络检测框架。该方法使分布式设备能够在不交换原始数据的情况下协同训练模型，从而在保障用户隐私的同时维持检测准确性。引入高效的通信聚合策略以降低开销，确保适用于资源受限的IoT网络。在基准IoT僵尸网络数据集上的实验表明，该框架在保持高检测准确率的同时，大幅降低了通信成本。结果表明，联邦学习为构建可扩展、安全且隐私友好的物联网入侵检测系统提供了切实可行的路径。
原文摘要 · Abstract (English)
The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these challenges, we present a lightweight and privacy-preserving botnet detection framework based on federated learning. This approach enables distributed devices to collaboratively train models without exchanging raw data, thus maintaining user privacy while preserving detection accuracy. A communication-efficient aggregation strategy is introduced to reduce overhead, ensuring suitability for constrained IoT networks. Experiments on benchmark IoT botnet datasets demonstrate that the framework achieves high detection accuracy while substantially reducing communication costs. These findings highlight federated learning as a practical path toward scalable, secure, and privacy-aware intrusion detection for IoT ecosystems.
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