SCADA Security
Machine Learning Concepts for Intrusion Detection and Prevention
By Abdulmohsen Almalawi, Adil Fahad, Xun Yi, Zahir Tari | Publisher: Wiley
About the book
Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems
Cyber-attacks on SCADA systems—the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management—can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning.
Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book:
- Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems
- Describes the relationship between main components and three generations of SCADA systems
- Explains the classification of a SCADA IDS based on its architecture and implementation
- Surveys the current literature in the field and suggests possible directions for future research
SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.
Editions of SCADA Security
- Hardcover
- ISBN 9781119606031
- EBook
- ISBN 9781119606383
- EBook
- ISBN 9781119606079
- EBook
- ISBN 9781119606352
Read an Excerpt
Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems
Cyber-attacks on SCADA systems—the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management—can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning.
Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book:
- Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems
- Describes the relationship between main components and three generations of SCADA systems
- Explains the classification of a SCADA IDS based on its architecture and implementation
- Surveys the current literature in the field and suggests possible directions for future research
SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.
Frequently Asked Questions
What is SCADA Security about?
Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems
Cyber-attacks on SCADA systems—the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management—can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning.
Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book:
- Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems
- Describes the relationship between main components and three generations of SCADA systems
- Explains the classification of a SCADA IDS based on its architecture and implementation
- Surveys the current literature in the field and suggests possible directions for future research
SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.
What core themes, tropes, or subjects are explored in SCADA Security?
Reference, Information and Interdisciplinary subjects > Research and information: general > Information theory > Cybernetics and systems theory
Where can I read a sample of SCADA Security?
You can read an official preview of the few pages here https://www.book2look.com/book/9781119606031
Is SCADA Security part of a series, and can it be read as a standalone?
Yes it is a part of series Wiley Series on Parallel and Distributed Computing
Who is/are the Author/s of the book SCADA Security?
Abdulmohsen Almalawi, Adil Fahad, Xun Yi, Zahir Tari
Who is the Publisher of the book SCADA Security?
Wiley
What are the ISBN numbers for the physical and digital editions?
SCADA Security is available as hardcover(ISBN 9781119606031) and ebook(ISBN 9781119606383) and ebook(ISBN 9781119606079) and ebook(ISBN 9781119606352)
Where can I buy SCADA Security online or support local independent bookshops?
You can buy SCADA Security from below
Where can I buy SCADA Security?