Linear Regression Trust Management System for IoT Systems

Author:

Subramanian Ananda Kumar1,Samanta Aritra1,Manickam Sasmithaa1,Kumar Abhinav1,Shiaeles Stavros2,Mahendran Anand3

Affiliation:

1. School of Computer Science and Engineering, Vellore Institute of Technology , India

2. Cyber Security and Resilience Research Group, School of Computing , University of Portsmouth , UK

3. Post-Doctoral Research Fellow, Laboratory of Theoretical Computer Science , HSE University , Moscow , Russia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A trust enhancement model based on distributed learning and blockchain in service ecosystems;Journal of King Saud University - Computer and Information Sciences;2024-09

2. SCLang: Graphical Domain-Specific Modeling Language for Stream Cipher;Cybernetics and Information Technologies;2023-06-01

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