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El. knyga: Machine Learning for Cyber Physical System: Advances and Challenges

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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. 

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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Pastovi nuoroda: https://www.kriso.lt/db/97830315403876e.html