Title: A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications,
Abstract: Rapid popularity of Internet of Things (IoT) and cloud computing permits
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 infrastructure, trust management is needed
at the IoT and user ends. This paper introduces a Neuro-Fuzzy based
Brain-inspired trust management model (TMM) to secure IoT devices and relay
nodes, and to ensure data reliability. The proposed TMM utilizes node
behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference
System and weighted-additive methods respectively to assess the nodes
trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2
simulation results confirm the robustness and accuracy of the proposed TMM in
identifying malicious nodes in the communication network. With the growing
usage of cloud based IoT frameworks in Neuroscience research, integrating the
proposed TMM into the existing infrastructure will assure secure and reliable
data communication among the E2E devices.