This book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field.
Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making.
This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff.
Cuprins
Introduction.- Machine Learning Construction: implications to cybersecurity.- Machine Learning Assessment: implications to cybersecurity.- A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms.- Unsupervised Anomaly Detection for MIL-STD-1553 Avionic Platforms using CUSUM.- Secure Design of Cyber-Physical Systems at the Radio Frequency Level: Machine and Deep Learning-Driven Approaches, Challenges and Opportunities.- Attack Detection by Using Deep Learning for Cyber-Physical System.- Security and privacy of Io T devices for ageing in place.- Detecting Malicious Attacks Using Principal Component Analysis in Medical Cyber-Physical Systems.- Activity and Event Network Graph and Application to Cyberphysical Security.
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