Authors:
Hanen Dhrir
1
;
Maha Charfeddine
2
and
Habib M. Kammoun
3
Affiliations:
1
Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, Sfax, Tunisia
;
2
REGIM-Lab: REsearch Groups in Intelligent Machines, National Engineering School of Sfax, Sfax, Tunisia
;
3
REGIM-Lab: REsearch Groups in Intelligent Machines, Faculty of Sciences of Sfax, Sfax, Tunisia
Keyword(s):
Anomaly Detection, Federated Learning, Deep Learning, Network Security, Privacy.
Abstract:
Network anomaly detection is a fundamental cybersecurity task that seeks to identify unusual patterns that could indicate security threats or system failures. Traditional centralized anomaly detection methods face issues such as data privacy. Federated Learning has emerged as a promising solution that distributes model training across multiple devices or nodes. Federated Learning improves anomaly detection by leveraging geographically distributed data sources while maintaining data privacy and security. This study presents a novel Federated Learning architecture designed specifically for network anomaly detection, addressing important information sensitivity issues in network environments. We compare some Deep Learning algorithms, such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Multilayer Perceptron (MLP), using XGBoost for feature selection and Stochastic Gradient Descent (SGD) as an optimizer. To address the problem of imbalanced data, we use the Syn
thetic Minority Over-sampling Technique (SMOTE) with the UNSW-NB15 dataset. Our methodology is rigorously evaluated using standard evaluation metrics and compared to state-of-the-art approaches.
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