Cyber Security Meets Machine Learning

Machine learning enhances security solutions but raises concerns about vulnerability to attacks and data privacy. This book explores recent research in machine learning for cybersecurity, including effective applications and urgent risks, divided into three parts. It is a valuable resource for researchers and practitioners in the field.

Format: Paperback / softback
Length: 163 pages
Publication date: 04 July 2022
Publisher: Springer Verlag, Singapore

Machine learning plays a pivotal role in enhancing the capabilities of security solutions in the modern cyber landscape. While it offers numerous advantages, it is important to acknowledge the security concerns associated with machine learning models and approaches. One significant concern is the vulnerability of machine learning models to adversarial attacks, which can exploit their weaknesses and lead to devastating consequences. Another pressing issue is the privacy of the data used in the training and testing periods, as it raises concerns about the potential misuse or unauthorized access of sensitive information.

To address these challenges, this book provides a comprehensive review of the latest research in the area. It explores effective applications of machine learning methods in cybersecurity solutions and highlights the urgent security risks related to machine learning models. The book is organized into three parts:

Cyber Security Based on Machine Learning: This section delves into the principles and techniques of machine learning for cybersecurity. It discusses the use of machine learning algorithms to detect and prevent cyber threats, such as malware, phishing attacks, and network intrusions. It also explores the role of machine learning in anomaly detection, user behavior analysis, and risk assessment.

Security in Machine Learning Methods and Systems: This section focuses on the security aspects of machine learning algorithms and systems. It examines the vulnerabilities and risks associated with different machine learning models, such as deep learning and neural networks. It also discusses the techniques and methods used to mitigate these risks, such as adversarial training, regularization, and ensemble modeling.

Security and Privacy in Outsourced Machine Learning: This section explores the security and privacy challenges associated with outsourcing machine learning services. It discusses the risks of data leakage, unauthorized access, and data manipulation during the outsourcing process. It also provides strategies and best practices for ensuring the security and privacy of outsourced machine learning models and data.

Written by leading researchers in the field, this book offers self-contained chapters that allow readers to select topics that are relevant to their specific needs. It serves as a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students, academic and industrial researchers, and practitioners seeking to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

In conclusion, machine learning has the potential to revolutionize the field of cybersecurity by enhancing the capabilities of security solutions. However, it is crucial to address the security concerns associated with machine learning models and approaches to ensure the trust and reliability of these technologies. By leveraging the latest research and best practices, we can develop more secure and resilient machine learning systems that can protect against emerging threats in the modern cyber environment.

Weight: 279g
Dimension: 235 x 155 (mm)
ISBN-13: 9789813367289
Edition number: 1st ed. 2021