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Federated Learning for Collaborative Cyber Defense
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Author(s): Syeda Mariam Muzammal (Taylor's University, Malaysia), Ruqia Bibi (University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan), Hira Waseem (University of Wah, Pakistan), Syed Nizam Ud Din (University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan), N. Z. Jhanjhi (School of Computer Science, Faculty of Innovation and Technology, Taylor's University, Malaysia)and Muhammad Tayyab (School of Computer Science, Faculty of Innovation and Technology, Taylor's University, Malaysia)
Copyright: 2025
Pages: 28
Source title:
Utilizing Generative AI for Cyber Defense Strategies
Source Author(s)/Editor(s): Noor Zaman Jhanjhi (Taylor's University, Malaysia)
DOI: 10.4018/979-8-3693-8944-7.ch001
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Abstract
With the increase in the complexity and number of cyber threats, security practitioners and defenders are looking for enhanced and robust security practices and solutions for effective cyber defense. Traditional cyber defense practices have been facing several challenges including data silos, data privacy concerns, and limited collaboration between organizations. In addition, the centralized machine learning models have their limitations, in terms of heterogeneity and formatting of datasets, for addressing distributed cyber threats. Federated learning has emerged as a decentralized machine learning approach with its potential for privacy preservation and contextualizing data for collaborative cyber defense. Federated learning has several characteristics, such as model aggregation, federated averaging, and differential privacy, which enable the collaborative training of machine learning models across distributed datasets.
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