Linear regression trust management system for IoT systems

Ananda Kumar Subramanian, Aritra Samanta, Sasmithaa Manickam, Abhinav Kumar, Stavros Shiaeles, Anand Mahendran

Research output: Contribution to journalArticlepeer-review

Abstract

This paper aims at creating a new Trust Management System (TMS) for a system of nodes. Various systems already exist which only use a simple function to calculate the trust value of a node. In the age of artificial intelligence the need for learning ability in an Internet of Things (IoT) system arises. Malicious nodes are a recurring issue and there still has not been a fully effective way to detect them beforehand. In IoT systems, a malicious node is detected after a transaction has occurred with the node. To this end, this paper explores how Artificial Intelligence (AI), and specifically Linear Regression (LR), could be utilised to predict a malicious node in order to minimise the damage in the IoT ecosystem. Moreover, the paper compares Linear regression over other AI-based TMS, showing the efficiency and efficacy of the method to predict and identify a malicious node.

Original languageEnglish
Pages (from-to)15-27
Number of pages13
JournalCybernetics and Information Technologies
Volume21
Issue number4
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

  • Internet of Things (IoT)
  • linear regression
  • node analysis
  • trust management
  • wireless sensor networks

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