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Privacy-Preserving Federated Learning in Network Intrusion Detection: A Systematic Literature Review
Faculty
Computer Science
Year:
2025
Type of Publication:
ZU Hosted
Pages:
Authors:
Eman selim
Staff Zu Site
Abstract In Staff Site
Journal:
International Journal of Computers and Informatics Zagazig University
Volume:
Keywords :
Privacy-Preserving Federated Learning , Network Intrusion Detection:
Abstract:
Machine learning privacy preservation is essential because it defends against misuse and illegal access to sensitive personal data including financial information, medical records, and behavioral patterns. Centralizing data in one place is necessary for traditional machine learning techniques, which poses serious privacy problems. Federated learning becomes an innovative approach in this situation. With federated learning, the model comes to the data rather than the other way around, radically altering the training process for machine learning systems. Individual devices or organizations where the data is naturally located are used for the training process rather than a central server. Each participant trains the model using local data, and only model updates are returned to the global model for updating. Raw data never leaves its original place, hence there is a far lower chance of data breaches during transfer. This article proposes a systematic review of federated learning with privacy preservation for intrusion detection. The three chosen online libraries of IEEE Xplorer, Scopus, and Web of Science are searched. Each database has its corresponding search query. The search is conducted to include all papers published since 2016 on computer science research area. The search results contain 220 papers from the different search engines. After removing duplicated papers, the search results are reduced to 131. Inclusion and exclusion criteria are then used to filter the search results. After applying the criteria of inclusion and exclusion, only 32 papers are approved.
Author Related Publications
Eman selim, "Evaluating Model Inversion Attack Success Across Neural Architectures in Federated Learning for Malware Classification", Springer Nature, 2025
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Eman selim, "A Survey of Federated Learning Privacy Preservation Techniques for Malicious Behavior Detection", International Association for Digital Transfor mation and Technological Innovation, 2025
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Eman selim, "A Lightweight Android Malware Classifier Using Novel Feature Selection Methods", MDPI, 2020
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Eman selim, "On Malware Detection on Android Smartphones", IJRASET, 2020
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Eman selim, "A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis", MDPI, 2025
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Department Related Publications
Osama Mohamed Abdelsalam Ahmed Elkomy, "MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans", IEEE, 2021
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Osama Mohamed Abdelsalam Ahmed Elkomy, "Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans.", ELSEVIER, 2021
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Osama Mohamed Abdelsalam Ahmed Elkomy, "Efficient model for emergency departments: Real case study", Computers, Materials and ContinuaComputers, Materials and Continua, 2022
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Ahmed Mahmoud Mahmoud Dawood, "SEMANTIC REPRESENTATION OF MUSIC DATABASE USING NEW ONTOLOGY-BASED SYSTEM", Journal of Theoretical and Applied Information Technology, 2020
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