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All Journal Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Sejaphala, Lanka Chris
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Machine Learning Algorithms to Defend Against Routing Attacks on the Internet of Things: A Systematic Literature Review Sejaphala, Lanka Chris; Malele, Vusimuzi; Lugayizi, Francis
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.828

Abstract

The Internet of Things (IoT) has become increasingly popular, opening vast application possibilities in different fields including smart cities, healthcare, manufacturing, agriculture, etc. IoT comprises resource-constrained devices deployed in Low Power and Lossy Networks (LLNs). To satisfy the routing requirements of these networks, the Internet Engineering Task Force (IETF) created a standardised Routing Protocol for low-power and Lossy Networks (RPL). However, this routing protocol is vulnerable to routing attacks, prompting researchers to propose several techniques to defend the network against such attacks. Machine learning approaches demonstrate effective ways to detect such attacks in large quantities. Therefore, this paper systematically synthesised 17 publications to compare the performance of traditional and advanced machine learning algorithms to identify the best algorithm for detecting RPL-based IoT routing attacks. The findings of this paper show that machine learning algorithms are capable of effective detection of many routing attacks with high accuracy and a low False Positive Rate. Furthermore, the results demonstrate that on average, advanced machine learning algorithms can achieve an accuracy of 96.03% compared to traditional machine learning algorithms which achieved 91.67%. Traditional machine learning algorithms demonstrated the best performance on average False Positive Rate by achieving 2.75% compared to their counterparts which gained 4.79%. However, Random Forest showed the best performance and outperformed all the algorithms in the selected publications by achieving over 99% accuracy, precision and recall.
High-Level Defence Model against Routing Attacks on the Internet-of-Things Sejaphala, Lanka Chris; Malele, Vusi; Lugayizi, Francis
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3744

Abstract

This paper is part of the doctoral work that aims to answer the following research question: “To what extent can an intelligent security model effectively defend against routing attacks in RPL-based Internet of Things (IoT) with a demonstration of less network resource consumption, high detection rate, and minimal false negatives?” To answer this question, this paper proposes a high-level conceptual framework to defend the IoT against routing attacks. In recent works, mitigation techniques have been proposed to act against routing attacks, however conceptual defence or mitigation framework is not presented as a set of steps to follow to develop an effective and robust intelligent security model. This paper aims to present a high-level conceptual defence framework against routing attacks; specifically, sinkhole, rank, DIS-Flooding, and worst parent. The four mentioned routing attacks are capable of disturbing IoT network functions and operations, and consuming network resources such as memory and power.
Co-Authors Lugayizi, Francis Malele, Vusi Malele, Vusimuzi