Description
Book SynopsisExamines 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
Table of Contents
Foreword ix
Preface xi
Acronyms xv
1. Introduction 1
2. Background 15
3. SCADA-Based Security Testbed 25
4. Efficient k-Nearest Neighbour Approach Based on Various-Widths Clustering 63
5. SCADA Data-Driven Anomaly Detection 87
6. A Global Anomaly Threshold to Unsupervised Detection 119
7. Threshold Password-Authenticated Secret Sharing Protocols 151
8. Conclusion 179
References 185
Index 195
