Neural Computing and Applications

, Volume 31, Supplement 1, pp 23–34 | Cite as

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

  • R. GiftyEmail author
  • R. Bharathi
  • P. Krishnakumar
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


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.


Big data Cyber physical systems (CPS) Security Privacy Intrusion detection Reliability Failure rate 


Compliance with ethical standards

Conflict of interest

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.


  1. 1.
    Giraldo J (2017) “Security and privacy in cyber-physical systems: a survey of surveys. IEEE Des Test 34(4):7–17. CrossRefGoogle Scholar
  2. 2.
    Howser G (2017) Using information-flow methods to analyze the security of cyber-physical systems. IEEE Comput 40(4):17–26. CrossRefGoogle Scholar
  3. 3.
    Jara AJ (2014) Big data for cyber physical systems—an analysis of challenges, solutions and opportunities. In: IEEE 8th international conference on innovative mobile and Internet services in ubiquitous computing, 2–4 July 2014, pp 376–380.
  4. 4.
  5. 5.
  6. 6.
    He H (2016) The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence. In: IEEE Congress on Evolutionary Computation, pp 1015–1021.
  7. 7.
    Puttonen J (2015) Security in cloud-based cyber-physical systems. In: IEEE 10th international conference on P2P, parallel, grid, cloud and Internet computing, pp 671–676.
  8. 8.
    Terzi DS (2015) A survey on security and privacy issues of big data. In: IEEE 10th international conference for Internet technology and secured transactions (ICITST), 14–16 December 2015.
  9. 9.
    Pasqualetti F (2015) Design and operation of secure cyber-physical systems. IEEE Embed Syst Lett 7(1):3–6. MathSciNetCrossRefGoogle Scholar
  10. 10.
    Pasqualetti F (2013) Attack detection and identification in cyber physical systems. IEEE Tran Autom Control 58(11):2715–2729. MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Wang EK (2010) Security issues and challenges for cyber physical system. In: IEEE/ACM international conference on cyber, physical and social computing, pp 733–738.
  12. 12.
    Humayed A (2017) Cyber physical systems security—a survey. IEEE Internet Things 1(99):1. Google Scholar
  13. 13.
    Eastman R (215) Big data and predictive analytics: on the cybersecurity front line. IDC Whitepaper, February 2015Google Scholar
  14. 14.
    Cheng P (2016) Cyber security for industrial control systems: from the viewpoint of close-loop, pp 133–145Google Scholar
  15. 15.
    AlTawy R (2016) Security trade-offs in cyber physical systems: a case study survey on implantable medical devices. IEEE Access 4:959–979. CrossRefGoogle Scholar
  16. 16.
    Kocabas O (2016) Emerging security mechanisms for medical cyber physical systems. IEEE Trans Comput Biol Bioinform 13(3):401–416. CrossRefGoogle Scholar
  17. 17.
    Elias (2016) A brief survey of security approaches for cyber-physical systems. In: IEEE 8th IFIP international conference on new technologies, mobility and security, pp 1–5.
  18. 18.
    Lu R, Liang X, Li X, Lin X, Shen X (2012) EPPA: an efficient and privacy preserving aggregation scheme for secure smart grid communications. IEEE Trans Parallel Distrib Syst 23(9):1621–1631CrossRefGoogle Scholar
  19. 19.
    Siddharth S (2012) Cyber-physical system security for the electric power grid. Proc IEEE 100(1):210–224CrossRefGoogle Scholar
  20. 20.
    Taylor C, Alves-Foss J (2001) NATE: network analysis of anomalous traffic events, a low-cost approach. In: Proceedings of workshop on new security paradigms, Cloudcroft, NM, pp 89–96Google Scholar
  21. 21.
    Evers K (2017) Security measurement on a cloud-based cyber-physical system used for intelligent transportation. In: IEEE international conference on vehicular electronics and safety, 27–28 June 2017, pp 97–102.
  22. 22.
    Mitchell R, Chen R (2014) Adaptive intrusion detection of malicious unmanned air vehicles using behavior rule specifications. IEEE Trans Syst Man Cybern Syst 44(5):593–604CrossRefGoogle Scholar
  23. 23.
    Kumar SAP (2017) Vulnerability assessment for security in aviation cyber-physical systems. In: IEEE 4th international conference on cyber security and cloud computing, 26–28 June 2017.
  24. 24.
    Cho C-S (2016) Cyberphysical security and dependability analysis of digital control systems in nuclear power plants. IEEE Trans Syst Man Cybern Syst 46(3):356–369. CrossRefGoogle Scholar
  25. 25.
    Shafi Q (2012) Cyber physical systems security: a brief survey. In: IEEE 12th international conference on computational science and its applications, pp 146–150.
  26. 26.
    Louthan G, Hardwicke P, Hawrylak P, Hale J (2011) Toward hybrid attack dependency graphs. In: The seventh annual workshop on cyber security and information intelligence research, CSIIRW’11, pp 62:1–62:1, Oak Ridge, TN, USA, October 2011Google Scholar
  27. 27.
    Li F, Clarke N, Papadaki M, Dowland P (2010) Behaviour profiling on mobile devices. In: International conference on emerging security technologies, pp 77–82, Canterbury, UK, September 2010Google Scholar
  28. 28.
    Kirkpatrick M, Ghinita G, Bertino E (2012) Resilient authenticated execution of critical applications in untrusted environments. IEEE Trans Dependable Secure Comput 9(4):597–609CrossRefGoogle Scholar
  29. 29.
    Gao Y (2013) Analysis of security threats and vulnerability for cyber-physical systems. In: IEEE Proceedings of 2013 3rd international conference on computer science and network theory, 12–13 Oct 2013.
  30. 30.
    Mitchell R (2013) Effect of intrusion detection and response on reliability of cyber physical systems. IEEE Trans Reliab 62(1):199–210. CrossRefGoogle Scholar
  31. 31.
    Jain P (2016) Big data privacy: a technological perspective and review. J Big Data. Google Scholar
  32. 32.
    Louvieris P, Clewley N, Liu X (2013) Effects-based feature identification fornetwork intrusion detection. Neurocomputing 121:265–273CrossRefGoogle Scholar
  33. 33.
    Mitchell R, Chen IR (2013) Survey of intrusion detection in wireless network applications. Elsevier Computer NetworksGoogle Scholar
  34. 34.
    Ma Y, Cao H, Ma J (2008) The intrusion detection method based on game theory in wireless sensor network. In: First IEEE international conference on Ubi-Media computing, pp 326–331, Lanzhou University, China, August 2008Google Scholar
  35. 35.
    Wang K, Stolfo S (2004) Anomalous payload-based network intrusion detection. In: Recent advances in intrusion detection, Sophia Antipolis, pp 203–222Google Scholar
  36. 36.
    Mitchell R, Chen IR, Eltoweissy M (2010) Signalprint-based intrusion detection in wireless networks. In: Security in emerging wireless communication and networking systems, pp 77–88, Athens, Greece, September 2010Google Scholar
  37. 37.
    Cabrera JBD (2004) On the statistical distribution of processing times in network intrusion detection. In: 43rd IEEE conference on decision and control.
  38. 38.
    Sivasamy AA (2015) A dynamic intrusion detection system based on multivariate hotelling’s T2 statistics approach for network environments. The Sci World J, vol 2015, Article ID 850153, 9 pp.

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Information and Communication EngineeringUniversity College of EngineeringNagercoilIndia
  2. 2.Electronics and Communication EngineeringUniversity College of EngineeringNagercoilIndia
  3. 3.Computer Science and EngineeringVV College of EngineeringThisayanvilai, TirunelveliIndia

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