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Privacy and security of big data in cyber physical systems using Weibull distribution-based intrusion detection

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • Published:
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Abstract

The volume of data collected from cyber physical systems (CPS) is huge, and we use big data techniques to manage and store the data. Big data in CPS is concerned with the massive heterogeneous data streams, which are acquired from autonomous sources and computed in distributed data storage system. In order to handle the size, complexity and rate of availability of data, it requires new techniques that can inspect and interpret useful knowledge from large streams of information, which impose challenges on the design and management of CPS in multiple aspects such as performance, energy efficiency, security, privacy, reliability, sustainability, fault tolerance, scalability and flexibility. This paper focuses on the security and privacy aspects in managing big data for CPS and reviews recent challenges in data privacy. We also present a protection framework for intrusion detection and analyze the performance parameters, reliability and failure rate in a malicious big data context.

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Authors and Affiliations

  1. Information and Communication Engineering, University College of Engineering, Nagercoil, Tamilnadu, 629004, India

    R. Gifty

  2. Electronics and Communication Engineering, University College of Engineering, Nagercoil, Tamilnadu, 629004, India

    R. Bharathi

  3. Computer Science and Engineering, VV College of Engineering, Thisayanvilai, Tirunelveli, India

    P. Krishnakumar

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  1. R. Gifty
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  2. R. Bharathi
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  3. P. Krishnakumar
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Correspondence to R. Gifty.

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The authors have no conflicts of interest to declare. I certify that no funding has been received for the conduct of this study and/or preparation of this manuscript.

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Gifty, R., Bharathi, R. & Krishnakumar, P. Privacy and security of big data in cyber physical systems using Weibull distribution-based intrusion detection. Neural Comput & Applic 31 (Suppl 1), 23–34 (2019). https://doi.org/10.1007/s00521-018-3635-6

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  • DOI: https://doi.org/10.1007/s00521-018-3635-6

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