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Machine Learning for Cyber Physical System: Advances and Challenges
Title
Machine Learning for Cyber Physical System: Advances and Challenges [electronic resource] / edited by Janmenjoy Nayak, Bighnaraj Naik, Vimal S, Margarita Favorskaya.
ISBN
9783031540387
Edition
1st ed. 2024.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Physical Description
1 online resource (XVI, 406 p.) 148 illus., 109 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book provides a comprehensive platform for learning the state-of-the-art machine learning algorithms for solving several cybersecurity issues. It is helpful in guiding for the implementation of smart machine learning solutions to detect various cybersecurity problems and make the users to understand in combating malware, detect spam, and fight financial fraud to mitigate cybercrimes. With an effective analysis of cyber-physical data, it consists of the solution for many real-life problems such as anomaly detection, IoT-based framework for security and control, manufacturing control system, fault detection, smart cities, risk assessment of cyber-physical systems, medical diagnosis, smart grid systems, biometric-based physical and cybersecurity systems using advance machine learning approach. Filling an important gap between machine learning and cybersecurity communities, it discusses topics covering a wide range of modern and practical advance machine learning techniques, frameworks, and development tools to enable readers to engage with the cutting-edge research across various aspects of cybersecurity. .
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
May 08, 2024
Series
Intelligent Systems Reference Library, 60
Contents
SMOTE Integrated Adaptive Boosting Framework for Network Intrusion Detection
An In-depth Analysis of Cyber-Physical Systems: Deep Machine Intelligence based Security Mitigations
Unsupervised approaches in anomaly detection
Profiling and Classification of IoT Devices for Smart Home Environments
Application of Machine Learning to Improve Safety in the Wind Industry
Malware Attack Detection in Vehicle Cyber Physical System for Planning and Control using Deep Learning
Unraveling what is at stake in the intelligence of autonomous cars
Intelligent Under-Sampling based Ensemble Techniques for Cyber-Physical Systems in Smart Cities
Application of Deep Learning in Medical Cyber-Physical Systems
Risk Assessment and Security of Industrial Internet of Things Network using Advance Machine Learning
Machine Learning Based Intelligent Diagnosis of Brain Tumor: Advances and Challenges
Cyber-Physical Security in Smart Grids: A Holistic View with Machine Learning Integration
Intelligent Biometric Authentication-based Intrusion Detection in Medical Cyber Physical System using Deep Learning
Current datasets and their inherent challenges for Automatic Vehicle Classification.
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