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.

Details

Title
Linear Regression Trust Management System for IoT Systems
Author
Subramanian, Ananda Kumar 1 ; Samanta, Aritra 1 ; Manickam, Sasmithaa 1 ; Kumar, Abhinav 1 ; Shiaeles, Stavros 2 ; Mahendran, Anand 3 

 School of Computer Science and Engineering, Vellore Institute of Technology, India 
 Cyber Security and Resilience Research Group, School of Computing, University of Portsmouth, UK 
 Post-Doctoral Research Fellow, Laboratory of Theoretical Computer Science, HSE University, Moscow, Russia 
Pages
15-27
Publication year
2021
Publication date
2021
Publisher
De Gruyter Poland
ISSN
13119702
e-ISSN
13144081
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3155662913
Copyright
© 2021. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.