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

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Machine Learning for Cyber Physical System: Advances and Challenges

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

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Abstract

Cyber Physical Systems (CPS) is a complex system whose components are both physical and software being intertwined has emerged as a crucial domain and is capable of exhibiting multiple and distinct behavioral modalities handling real-world applications. Over the past two decades, they have evolved into a cornerstone for research and industrial applications, embodying a convergence of physical, biological, and engineered components governed by a computational core. It is a network of interacting elements with physical input and output devices. These systems heavily rely on advanced sensor nodes, communication technologies and control units. Addressing the challenge of deploying sensors in spatially-distributed processes, wireless sensor networks (WSNs) have taken center stage in CPS. WSNs offer a cost-effective solution for monitoring a diverse range of applications, from battlefield surveillance to environmental oversight. The integration of sensor devices within CPS is pivotal in ensuring precision in control and enhancing reliability. Several transdisciplinary approaches like merging theory of cybernetics, mechatronics, design and process science are involved in a CPS and the process is called as embedded system. Concurrently, technological progress has given rise to sophisticated cyber threats, necessitating ongoing vigilance from researchers to safeguard both physical and virtual systems. This necessitates security mitigation to take measures for reducing these harmful effects or hazards. Deep machine intelligence means machine intelligence based upon deep learning techniques and is most recent AI techniques. This chapter delves into the challenges faced from the security aspects of CPS and their solutions based upon deep machine intelligence, presenting experimental findings through an intrusion detection dataset.

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Authors and Affiliations

  1. School of Computer Science Engineering and Information Systems, VIT, Vellore, Tamil Nadu, 632014, India

    B. K. Tripathy

  2. MITS School of Biotechnology, Bhubaneswar, Odisha, 751024, India

    G. K. Panda

  3. Research Scholar, Utkal University, Bhubaneswar, Odisha, 751004, India

    Ashok Sahu

Authors
  1. B. K. Tripathy
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  2. G. K. Panda
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  3. Ashok Sahu
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Corresponding author

Correspondence to B. K. Tripathy .

Editor information

Editors and Affiliations

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

    Janmenjoy Nayak

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

    Bighnaraj Naik

  3. Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Vimal S

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

    Margarita Favorskaya

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Tripathy, B.K., Panda, G.K., Sahu, A. (2024). An In-Depth Analysis of Cyber-Physical Systems: Deep Machine Intelligence Based Security Mitigations. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_2

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