Federated Learning for Distributed Anomaly  Detection in Network Traffic Using GRU-Based  Models

Hamad Riaz; Muhammad Zunnurain Hussain; Muhammad Zulkifl Hasan; Muzzamil Mustafa

Federated Learning for Distributed Anomaly Detection in Network Traffic Using GRU-Based Models

Authors

Hamad Riaz
Muhammad Zunnurain Hussain
Muhammad Zulkifl Hasan
Muzzamil Mustafa

Keywords:

Federated Learning, GRU-Based Models, Network Traffic Anomaly Detection, IoT Security, Privacy Preserving, Edge-IIoTset Dataset, Distributed Computing

Abstract

In this work, we present a novel machine learning method for anomaly detection in network traffic based on GRU based federated learning. Our decentralized method is supported by extensive experimental results and comparisons with existing techniques, and successfully addresses scenarios where centralized servers are not feasible due to privacy concerns or other constraints, and successfully detects anomalies in the distributed environments..

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Published

2025-03-31

How to Cite

Hamad Riaz, Muhammad Zunnurain Hussain, Muhammad Zulkifl Hasan, & Muzzamil Mustafa. (2025). Federated Learning for Distributed Anomaly Detection in Network Traffic Using GRU-Based Models. Spectrum of Engineering Sciences, 3(3), 522–534. Retrieved from https://sesjournal.com/index.php/1/article/view/230