Enhancing IoT Security through Anomaly-based Intrusion Detection Systems

Keloth Poyil, Muhammed Musthafa (2024) Enhancing IoT Security through Anomaly-based Intrusion Detection Systems. Masters thesis, Dublin, National College of Ireland.

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Abstract

The advancement of the Internet of Things (IoT) has seen rapid growth in the industrial connectivity and automation rates considerably. However, this growth has also created important concrete cybersecurity threats as IoT networks are now in the crosshairs of highly developed cyber attacks. The first problem is that, unlike more traditional networks, the emerging IoT networks exhibit high levels of heterogeneity and low available resources; and the second is that most current IDSs have rigid architecture and are not suitable for the IoT networks. This work offers an anomaly-based IDS for improving the security of IoT networks that exploits state-of-the-art ML and DL methodologies. The proposed system includes Gradient Boosting Machine (GBM), k-Nearest Neighbour (KNN), and Naive Bayes with Graph Neural Networks (GNNs): Graph Convolutional Networks (GCNs) and Graph Isomorphism Networks (GINs). In results of experiments, Random Forest and KNN surpass competitors with such diagrams as 96.30% and 98.19% correspondingly, while GNNs are also combined with GIN and give excellent results in resect of complex traffic pattern detection with 79.12% of accurate classification. These results prove that hybrid anomaly-based IDSs are useful to achieve a steady and efficient IoT cybersecurity model.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 16:16
Last Modified:	02 Sep 2025 16:16
URI:	https://norma.ncirl.ie/id/eprint/8727

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