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Federated Learning for Network Anomaly Detection in a Distributed Industrial Environment


Publication Type:

Conference/Workshop Paper


International Conference on Machine Learning and Applications 23


Industrial control systems have been targeted by numerous cyber attacks over the past few decades which causes different problems related to data privacy, financial losses and operational failures. One potential approach to detect these attacks is by analyzing network data using machine learning and employing network anomaly detection techniques. However, the nature of these systems often involves their geographical dispersion across multiple zones, which poses a challenge in applying local machine learning methods for detecting anomalies. Additionally, there are instances where sharing complete operational data between different zones is restricted due to security concerns. As a result, a promising solution emerges by implementing a federated model for anomaly detection in these systems. In this study, we investigate the application of machine learning techniques for anomaly detection in network data, considering centralized, local, and federated approaches. We implemented the local and centralized methods using several simple machine-learning techniques and observed that Random Forest and Artificial Neural Networks exhibited superior performance compared to other methods. As a result, we extended our analysis to develop a federated version of Random Forest and Artificial Neural Network. Our findings reveal that the federated model surpasses the performance of the local models, and achieves comparable or even superior results compared to the centralized model, while it ensures data privacy and maintains the confidentiality of sensitive information.


@inproceedings{Dehlaghi Ghadim6853,
author = {Alireza Dehlaghi Ghadim and Tijana Markovic and Miguel Leon Ortiz and David S{\"o}derman and Per Erik Strandberg},
title = {Federated Learning for Network Anomaly Detection in a Distributed Industrial Environment},
month = {December},
year = {2023},
booktitle = {International Conference on Machine Learning and Applications 23},
url = {}