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Machine Learning for Cyber Physical System: Advances and Challenges

  • Book
  • © 2024

Overview

Editors:
  1. Janmenjoy Nayak

    1. Department of Computer Science, MSCB University, Baripada, India

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  2. Bighnaraj Naik

    1. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, India

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  3. Vimal S

    1. Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, India

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  4. Margarita Favorskaya

    1. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

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  • 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

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 60)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xvi
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  2. SMOTE Integrated Adaptive Boosting Framework for Network Intrusion Detection

    • Suresh Kumar Pemmada, K. Sowjanya Naidu, Dukka Karun Kumar Reddy
    Pages 1-25

  3. An In-Depth Analysis of Cyber-Physical Systems: Deep Machine Intelligence Based Security Mitigations

    • B. K. Tripathy, G. K. Panda, Ashok Sahu
    Pages 27-55

  4. Unsupervised Approaches in Anomaly Detection

    • Juan Ramón Bermejo Higuera, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Rubén González Crespo
    Pages 57-83

  5. Profiling and Classification of IoT Devices for Smart Home Environments

    • Sudhir Kumar Das, Sujit Bebortta, Bibudhendu Pati, Chhabi Rani Panigrahi, Dilip Senapati
    Pages 85-121

  6. Application of Machine Learning to Improve Safety in the Wind Industry

    • Bertrand David Barouti, Seifedine Kadry
    Pages 123-165

  7. Malware Attack Detection in Vehicle Cyber Physical System for Planning and Control Using Deep Learning

    • Challa Ravi Kishore, H. S. Behera
    Pages 167-193

  8. Unraveling What is at Stake in the Intelligence of Autonomous Cars

    • Dioneia Motta Monte-Serrat, Carlo Cattani
    Pages 195-217

  9. Intelligent Under Sampling Based Ensemble Techniques for Cyber-Physical Systems in Smart Cities

    • Dukka Karun Kumar Reddy, B. Kameswara Rao, Tarik A. Rashid
    Pages 219-244

  10. Application of Deep Learning in Medical Cyber-Physical Systems

    • H. Swapnarekha, Yugandhar Manchala
    Pages 245-265

  11. Risk Assessment and Security of Industrial Internet of Things Network Using Advance Machine Learning

    • Geetanjali Bhoi, Rajat Kumar Sahu, Etuari Oram, Noor Zaman Jhanjhi
    Pages 267-285

  12. Machine Learning Based Intelligent Diagnosis of Brain Tumor: Advances and Challenges

    • Surendra Kumar Panda, Ram Chandra Barik, Danilo Pelusi, Ganapati Panda
    Pages 287-312

  13. Cyber-Physical Security in Smart Grids: A Holistic View with Machine Learning Integration

    • Bhaskar Patnaik, Manohar Mishra, Shazia Hasan
    Pages 313-344

  14. Intelligent Biometric Authentication-Based Intrusion Detection in Medical Cyber Physical System Using Deep Learning

    • Pandit Byomakesha Dash, Pooja Puspita Priyadarshani, Meltem Kurt Pehlivanoğlu
    Pages 345-375

  15. Current Datasets and Their Inherent Challenges for Automatic Vehicle Classification

    • Sourajit Maity, Pawan Kumar Singh, Dmitrii Kaplun, Ram Sarkar
    Pages 377-406
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Keywords

About this book

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. 

Editors and Affiliations

  • Department of Computer Science, MSCB University, Baripada, India

    Janmenjoy Nayak

  • Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, India

    Bighnaraj Naik

  • Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, India

    Vimal S

  • Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita Favorskaya

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