The rapid expansion of the Internet of Things (IoT) ecosystem has transformed various sectors but has also introduced significant cybersecurity challenges. Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. To address these issues, this study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. The framework enables decentralized data processing by training models locally on edge devices, ensuring data privacy. Secure aggregation of these locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information. The proposed framework utilizes recurrent neural networks (RNNs) for anomaly detection, optimized for resource-constrained IoT networks. Experimental results demonstrate that the system effectively detects complex cyber threats, including distributed denial-of-service (DDoS) attacks, with over 98% accuracy. Additionally, it improves energy efficiency by reducing resource consumption by 20% compared to centralized approaches. This research addresses critical gaps in IoT cybersecurity by integrating federated learning with advanced threat detection techniques. The framework offers a scalable and privacy-preserving solution adaptable to various IoT applications. Future work will explore the integration of blockchain for transparent model aggregation and quantum-resistant cryptographic methods to further enhance security in evolving technological landscapes.

翻译：物联网生态系统的快速扩张已深刻改变多个行业领域，但同时也带来了严峻的网络安全挑战。传统的集中式安全方法在物联网网络中往往难以兼顾隐私保护与实时威胁检测。为应对这些问题，本研究提出一种专为物联网环境设计的联邦学习驱动网络安全框架。该框架通过在边缘设备本地训练模型实现去中心化数据处理，从而确保数据隐私。通过采用同态加密技术实现本地训练模型的安全聚合，使得协作学习过程无需暴露敏感信息。所提出的框架利用循环神经网络进行异常检测，并针对资源受限的物联网网络进行了优化。实验结果表明，该系统能有效检测包括分布式拒绝服务攻击在内的复杂网络威胁，准确率超过98%。相较于集中式方法，该框架通过降低20%的资源消耗提升了能效。本研究通过将联邦学习与先进威胁检测技术相结合，弥补了物联网网络安全的关键空白。该框架提供了一种可扩展且保护隐私的解决方案，可适配多种物联网应用场景。未来工作将探索集成区块链技术以实现透明化模型聚合，并研究抗量子密码方法，以在持续演进的技术环境中进一步增强安全性。