Machine Learning for Cyber Physical System: Advances and Challenges: Intelligent Systems Reference Library, cartea 60

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.

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. 

Provides an insight into applications as well as state-of-the-art research findings of machine learning in the CPS Presents a single platform for learning the state-of-the-art machine algorithms for solving cybersecurity issues Is helpful in guiding for the implementation of smart machine learning solutions