Linear Regression Trust Management System for IoT Systems

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ananda Kumar Subramanian, Aritra Samanta, Sasmithaa Manickam, Abhinav Kumar, S. Shiaeles, Anish Mahendran
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引用次数: 1

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.
物联网系统线性回归信任管理系统
摘要本文旨在为节点系统创建一种新的信任管理系统(TMS)。已经有很多系统只使用一个简单的函数来计算节点的信任值。在人工智能时代,对物联网(IoT)系统学习能力的需求出现了。恶意节点是一个反复出现的问题,目前还没有一种完全有效的方法来预先检测它们。在物联网系统中,在与节点发生交易后检测到恶意节点。为此,本文探讨了如何利用人工智能(AI),特别是线性回归(LR)来预测恶意节点,以尽量减少物联网生态系统中的损害。此外,本文还将线性回归与其他基于人工智能的TMS进行了比较,显示了该方法预测和识别恶意节点的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
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