Optimising Gated Recurrent Unit for Intrusion Detection in Internet of Things Networks: A Comparative Analysis with Other Deep Learning-Based Methods

Murugan, Jothybala (2024) Optimising Gated Recurrent Unit for Intrusion Detection in Internet of Things Networks: A Comparative Analysis with Other Deep Learning-Based Methods. Masters thesis, Dublin, National College of Ireland.

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Abstract

The research project’s primary goal is to protect the Internet of Things devices from the cyber-attacks using the advanced neural networks. This paper was discussed about the increase in accuracy as well as effectiveness of the intrusion detection in Internet of Things (IoT) network by optimising the design and hyperparameter of Gated recurrent units (GRUs). This research work was fully dedicated to the growth of an attack classifier, which was the intrusion detection system. The rapidly increasing in the number of cyber-attacks have made intrusion detection prediction research essential. It is important to maintain the security as well as integrity of the Internet of Things (IoT). Even though for predicting the intrusions there are many ways to solve this problem but the most efficient way we used in this research to solve this is Gated Recurrent Units (GRUs) which is a gating mechanism in deep learning. In this deep learning technique, we have used a specifically used the Bidirectional GRU and convolutional gated recurrent unit (ConvGRU). The different network traffic data in the existing RT-IoT dataset, we evaluated the models based on different type of attack scenario. The gated recurrent unit’s models were trained in this research and evaluated the model’s accuracy using the IoT dataset. A deep learning model based on Gated Recurrent Units (GRUs) is explained and showed results can predict future alert probabilities from an attacking source. A comparison was evaluated using evaluation metrics and from the analysis the Convolution gated recurrent unit performed well than the other two models and detect the IoT attacks with a precision of 98%, F1 Score of 98.3% and an accuracy of 98% with minimum loss value of 0.04 respectively. Based on the evaluation, using the deep learning methods, we can conclude that the intrusion detection in the IoT can be solved successfully. This entire research handled with the ethical issues which include data confidentiality as well as reliability.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Yaqoob, Abid UNSPECIFIED
Uncontrolled Keywords:	Internet of Things; Gated Recurrent Units; intrusion detection; deep learning
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:	20 Aug 2025 11:41
Last Modified:	20 Aug 2025 11:41
URI:	https://norma.ncirl.ie/id/eprint/8595

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