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

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)864–873
Number of pages10
JournalCognitive Computation
Volume10
Issue number5
Early online dateFeb 2018
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Keywords

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

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