Machine Learning for Cyber Physical System: Advances and Challenges / edited by Janmenjoy Nayak, Bighnaraj Naik, Vimal S, Margarita Favorskaya.

Format:

Book

Contributor:

Nayak, Janmenjoy, editor.

Series:

Intelligent Systems Reference Library, 1868-4408 ; 60

Language:

English

Subjects (All):

Computational intelligence.

Cooperating objects (Computer systems).

Machine learning.

Artificial intelligence.

Computational Intelligence.

Cyber-Physical Systems.

Machine Learning.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Cyber-Physical Systems.

Machine Learning.

Artificial Intelligence.

Physical Description:

1 online resource (412 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

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. .

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.

Notes:

Includes bibliographical references.

ISBN:

3-031-54038-7