A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications
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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.
KeywordsANFIS Neuro-fuzzy system Cybersecurity Behavioral trust Data trust Quality of service Neuroscience big data Brain research
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.
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.
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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.
This article does not contain any studies with human participants or animals performed by any of the authors.
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.
- 9.Young AL, Oxtoby NP, Schott JM, Alexander DC. Data-driven models of neurodegenerative disease. Adv Clin Neurosci Rehabil 2014;14(5):6–9.Google Scholar
- 14.Luo B, Hussain A, Mahmud M, Tang J. Advances in brain-inspired cognitive systems. Cogn Comput 2016;8(5):795–796.Google Scholar
- 23.Shabut AM, Dahal K. Social factors for data sparsity problem of trust models in MANETs. Proceedings of the ICNC; 2017. p. 876–880.Google Scholar
- 29.Ruan Y, Durresi A, Alfantoukh L. Trust management framework for internet of things. Proceedings of the AINA; 2016. p. 1013–1019.Google Scholar
- 30.Chen IR, Guo J, Bao F, Cho JH. Integrated social and quality of service trust management of mobile groups in ad hoc networks. Proceedings of the ICICS; 2013. p. 1–5.Google Scholar
- 34.Afsana F, Jahan N, Sunny FA, Kaiser MS, Mamun SA. Trust and energy aware Cluster modeling and spectrum handoff for cognitive radio ad-hoc network. Proceedings of the ICEEICT; 2015. p. 1–6.Google Scholar
- 39.Momani M, Takruri M, Al-Hmouz R. Risk assessment algorithm in wireless sensor networks using beta distribution. CoRR 2014. arXiv:1410.3041.
- 40.Liu Y, Chitawa US, Guo G, Wang X, Tan Z, Wang S. A Reputation Model for Aggregating Ratings Based on Beta Distribution Function. Proceedings of the ICCSE; 2017. p. 77–81.Google Scholar
- 41.Josang A, Ismail R. The beta reputation system. Proceedings of the BLED; 2002. p. 324–337.Google Scholar
- 46.Kaur R, Sharma N. Dynamic node recovery for improved throughput in MANET. Proceedings of the NGCT; 2015. p. 325–330.Google Scholar
- 47.Gupta NK, Pandey K. Trust based Ad-hoc on Demand Routing protocol for MANET. Proceedings of the IC3; 2013. p. 225–231.Google Scholar
- 48.Talreja R, Sathish S, Nenwani K. Trust Variable Factor : A trust based method to detect misbehaving nodes in MANET. Proceedings of the ICEEOT; 2016. p. 3238–3241.Google Scholar
- 51.Gu Q, Zhu L, Cai Z. Computational Intelligence and Intelligent Systems. Evaluation measures of the classification performance of imbalanced data sets. Berlin: Springer; 2009. p. 461–471.Google Scholar