SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention

SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a book that examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems. It discusses how cyber-attacks on SCADA systems can lead to costly financial consequences or even result in loss of life, and emphasizes the need for innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. The book is organized into eight in-depth chapters that cover essential SCADA concepts, systems, architectures, and main components, as well as various SCADA security frameworks and approaches. It provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems and explores the relationship between main components and three generations of SCADA systems. The book also surveys the current literature in the field and suggests possible directions for future research.

Format: Hardback
Length: 224 pages
Publication date: 25 February 2021
Publisher: John Wiley and Sons Ltd

Cyber-attacks on Supervisory Control and Data Acquisition (SCADA) systems, which employ computers, networked data communications, and graphical user interfaces for high-level process supervisory management, can have severe financial consequences or even result in loss of life. To mitigate potential risks and respond to malicious actions effectively, innovative approaches for monitoring SCADA systems and safeguarding them against targeted attacks are essential.

SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a comprehensive guide designed to assist security and networking professionals in developing and deploying accurate and effective Intrusion Detection Systems (IDS) for SCADA systems. The book offers expert insights and practical advice, covering the latest developments in SCADA security.

Organized into eight in-depth chapters, the text begins by discussing how traditional IT attacks can also impact SCADA systems. It then delves into essential SCADA concepts, systems, architectures, and main components. Subsequent chapters explore various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, utilizing SDAD for proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more.

This important book provides a diverse range of perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems. It also elucidates the relationship between main components and the three generations of SCADA systems. Furthermore, it offers a comprehensive classification of anomaly detection techniques, aiding security professionals in selecting the most suitable methods for their SCADA systems.

By leveraging autonomous machine learning, SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention empowers security and networking professionals to enhance the security posture of their SCADA systems and protect against emerging threats. This authoritative guide is a valuable resource for professionals, researchers, and students interested in SCADA security and machine learning.

Weight: 466g
Dimension: 156 x 235 x 18 (mm)
ISBN-13: 9781119606031