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Smart city article details
| Title | Federated Learning For Enhanced Intrusion Detection In Smart City Environments |
|---|---|
| ID_Doc | 26340 |
| Authors | Hamid S.; Bawany N.Z. |
| Year | 2024 |
| Published | 2024 18th International Conference on Open Source Systems and Technologies, ICOSST 2024 - Proceedings |
| DOI | http://dx.doi.org/10.1109/ICOSST64562.2024.10871154 |
| Abstract | The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection Systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyconscious IDS, ensuring effective real-time threat detection while preserving data privacy and reducing power consumption in smart city environments. © 2024 IEEE. |
| Author Keywords | AI in Smart Cities; Data Privacy; Distributed AI; Federated Learning; Intrusion Detection System (IDS) |
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