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Assistant Professor, Kalinga University , Naya Raipur, Chhattisgarh , India
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Assistant Professor, Kalinga University , Naya Raipur, Chhattisgarh , India
Assistant Professor, Department of Computer Science and Engineering, Sona College of Technology , Salem, Tamil Nadu , India
The high rate of Internet of Things (IoT) device proliferation in smart cities, along with the emergence of 6G technology, has tremendously augmented the network traffic and the issue of security. This paper proposes a Federated Learning-based Intrusion Detection System (FL-IDS) specifically designed for 6G-enabled IoT networks. The new system helps to solve the problem of scalability, privacy protection, and the possibility of detecting anomalies in time without any central storage of the data. FL allows training local models on edge devices and only the weights are transferred to a central model, which allows preserving sensitive information privacy. The methodology involves the use of intrusion detection using the local models of Random Forest, SVM, and KNN, and trained locally on the IoT devices. These models are subsequently federated by averaging to create a federated global model to be effective in detecting intrusion in large-scale IoT networks. The system identifies the anomalies of Denial of Service (DoS), spoofing, and data breach based on the network traffic patterns and device behavior variation. The system was evaluated using key performance metrics, namely, accuracy, precision, recall, and F1-score. These findings prove that the FL-IDS can attain an accuracy of 98, an increase of 12 % over the traditional intrusion detection systems. The system also decreases false positive rates by 20 %, as well as communication overhead by 35 %. The federated learning architecture enables scalable and efficient deployment, a large amount of data processing, and data privacy. Finally, the FL-based intrusion detection system provides a solution that is privacy-saving, scalable, and real-time to detect intrusion in 6G IoT networks in smart cities. The further study will concentrate on the optimization of the model updates and the system performance in dynamic urban settings, as well as real-time monitoring.
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