Abstract
The proliferation of Internet of Things (IoT) devices in smart home environments has significantly expanded the attack surface, making home networks increasingly susceptible to sophisticated cyber threats. Traditional cloud-based intrusion detection systems (IDS) often suffer from high latency and performance bottlenecks, limiting their effectiveness in real-time threat mitigation. This paper introduces a Hierarchical Federated Generative Learning (HFGL) framework for decentralized intrusion detection, leveraging a multi-tiered architecture composed of user routers, a local server, and edge nodes to facilitate real-time network monitoring and distributed anomaly detection. The system integrates OpenWRT-based router firmware with packet-level data capture, securely transmitting network traffic metadata to a local server via SSH-based Paramiko requests. A custom Electron.js-based desktop application provides an intuitive interface for homeowners, enabling seamless router configuration, real-time alert visualization, and security policy orchestration. At its core, our approach employs a federated deep learning pipeline augmented with Generative Adversarial Networks (GANs) to enhance intrusion detection capabilities while preserving data privacy. The GAN model not only identifies emerging threats but also generates adversarial attack simulations to improve model robustness. Experimental evaluations demonstrate significant improvements in detection accuracy and latency reduction compared to conventional IDS approaches, underscoring the potential of privacy-preserving, edge-driven cybersecurity solutions for modern smart home ecosystems.
