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Cognitive Computation

, Volume 10, Issue 5, pp 864–873 | Cite as

A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications

  • Mufti Mahmud
  • M. Shamim Kaiser
  • M. Mostafizur Rahman
  • M. Arifur Rahman
  • Antesar Shabut
  • Shamim Al-Mamun
  • Amir Hussain
Article

Abstract

Rapid advancement of Internet of Things (IoT) and cloud computing enables neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructures, trust management is needed at the IoT and user ends. This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes both node behavioral trust and data trust, which are estimated using ANFIS, and weighted additive methods respectively, to assess the nodes trustworthiness. In contrast to existing fuzzy based TMMs, simulation results confirm the robustness and accuracy of our proposed TMM in identifying malicious nodes in the communication network. With growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into existing infrastructure will assure secure and reliable data communication among E2E devices.

Keywords

ANFIS Neuro-fuzzy system Cybersecurity Behavioral trust Data trust Quality of service Neuroscience big data Brain research 

Notes

Acknowledgments

The work was supported by ACS Lab (http://www.acslab.info). The authors would like to acknowledge members of the ACS Lab for proof-reading the manuscript. Amir Hussain was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through grant numbers EP/I009310/1 and EP/M026981/1.

Author Contributions

This work was carried out in close collaboration between all co-authors. MM, MSK, MMR, MAR, and SAM first defined the research theme and contributed an early design of the system. MSK and AS further implemented and refined the system development. MM and MSK first drafted the paper and all authors edited the draft. All authors have contributed to, seen, and approved the final manuscript.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

As this article does not contain any studies with human participants or animals performed by any of the authors, the informed consent in not applicable.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.NeuroChip Lab, Department of Biomedical SciencesUniversity of PadovaPadovaItaly
  2. 2.Institute of Information TechnologyJahangirnagar UniversityDhakaBangladesh
  3. 3.School of MathematicsAmerican International University - BangladeshDhakaBangladesh
  4. 4.Department of Computer ScienceUniversity of SheffieldSheffieldUK
  5. 5.Anglia Ruskin UniversityChelmsfordUK
  6. 6.Saitama UniversitySaitamaJapan
  7. 7.University of StirlingStirlingUK

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