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Linear Regression Trust Management System for IoT Systems Cover

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.

DOI: https://doi.org/10.2478/cait-2021-0040 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 15 - 27
Submitted on: Apr 26, 2021
Accepted on: Oct 12, 2021
Published on: Dec 9, 2021
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Ananda Kumar Subramanian, Aritra Samanta, Sasmithaa Manickam, Abhinav Kumar, Stavros Shiaeles, Anand Mahendran, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.