Название: Federated Cyber Intelligence: Federated Learning for CybersecurityАвтор: Hamed Tabrizchi, Ali Aghasi
Издательство: Springer
Серия: SpringerBriefs in Computer Science
Год: 2025
Страниц: 119
Язык: английский
Формат: pdf (true), epub (true), mobi
Размер: 10.1 MB
This book offers a detailed exploration of how Federated Learning can address critical challenges in modern cybersecurity. It begins with an introduction to the core principles of Federated Learning (FL). Then it highlights a strong foundation by exploring the fundamental components, workflow, and algorithms of Federated Learning, alongside its historical development and relevance in safeguarding digital systems.
The subsequent sections offer insight into key cybersecurity concepts, including confidentiality, integrity, and availability. It also offers various types of cyber threats, such as malware, phishing, and advanced persistent threats. This book provides a practical guide to applying Federated Learning in areas such as intrusion detection, malware detection, phishing prevention, and threat intelligence sharing. It examines the unique challenges and solutions associated with this approach, such as data heterogeneity, synchronization strategies and privacy-preserving techniques.
In an era where data is more valuable than gold, the protection and ethical use of data have become essential. With FL, a whole new way of analyzing data has opened up, promising a new paradigm for privacy, security, and collaboration. Federated Learning is a Machine Learning setting, in which the goal is to train a model across a variety of decentralized devices or servers that contain local data samples, without exchanging them. Thus, many privacy and security concerns inherent in traditional Machine Learning models can be addressed without ever centralizing data. Moreover, the field of communication and networking is eagerly seeking Machine Learning-based decision-making solutions. These are seen as a replacement for the traditional model-driven methods, which have been found inadequate in capturing the increasing complexity and diversity of contemporary systems in the field. On the other hand, traditional Machine Learning solutions typically rely on central entities, often cloud-based, to process data. However, the challenges associated with accessing private data and the substantial costs of transmitting raw data to the central entity have led to the emergence of a decentralized Machine Learning method known as Federated Learning.
Federated Learning was developed in response to the requirement to make use of the enormous amount of data that is generated every day across a wide range of devices, such as smartphones and Internet of Things devices while respecting the security and right to privacy of the user. Traditional Machine Learning approaches require centralized data storage, which poses significant privacy risks and logistical challenges. Federated learning, on the other hand, allows a model to be trained across multiple devices by using their computational resources and data without moving the data itself. Aside from addressing privacy concerns, this new training paradigm also offers new opportunities for collaborative intelligence across diverse sectors and entities
This book starts with Chapter 1, which explains the fundamental ideas of federated learning. Chapter 2 elucidates the essential methodological and technical components of federated learning. Chapter 3 pertains to cybersecurity, providing essential insights into the principles, challenges, and evolving landscape of cyber defense. Chapter 4 analyzes the impact of federated learning on modern cybersecurity systems. It demonstrates its capacity to identify and alleviate dangers using decentralized intelligence. Chapter 5 ultimately contemplates the insights acquired and analyzes the forthcoming trajectory, emphasizing future problems and possibilities in federated cyber intelligence.
This book is intended for scholars and educators aiming to comprehend the relationship between Federated Learning and cybersecurity.
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