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


Article Information

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

Authors: Hamad Riaz, Muhammad Zunnurain Hussain, Muhammad Zulkifl Hasan, Muzzamil Mustafa

Journal: Spectrum of Engineering Sciences

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31

Publisher: Sociology Educational Nexus Research Institute

Country: Pakistan

Year: 2025

Volume: 3

Issue: 3

Language: en

Keywords: Federated LearningIoT SecurityDistributed computingPrivacy PreservingGRU-Based ModelsNetwork Traffic Anomaly DetectionEdge-IIoTset Dataset

Categories

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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