Robust Intrusion Detection Model for Internet of Things

Jaiyeola, Ayobami (2021) Robust Intrusion Detection Model for Internet of Things. Masters thesis, Dublin, National College of Ireland.

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Abstract

As the development and usage of Internet of Things (IoT) continues to rise globally, the need for security of data and devices has risen simultaneously. Currently there are intrusion tolerance systems which have been provided tackle these issues; sadly, it remains a difficult task for these systems to develop a trusted interconnection between many IoT nodes due to the extremely massive number and vigorous make-up of devices. To pull off a better performance in intrusion detection, collaborative IDSs are often deployed in practical scenarios, where intrusion tolerance systems nodes are allowed to distribute essential information between them. In this research, a hybrid intrusion detection system has been proposed. It comprises of two stages of detection. Firstly, anomaly detection is carried out by using Spark Machine Learning; the next step involves deploying Convolutional LSTM network for misuse detection. After carrying out an experiment where the system will be fed with an up-to-date and suitable dataset; analysis will be carried out by using four performance metrics to create a confusion matrix that will be used evaluate the system’s performance. It is expected at the end of this study that the proposed hybrid architecture will show higher accuracy when compared to similar approaches to intrusion detection.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Intrusion Detection System (IDS); Internet of Things (IoT)
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
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Clara Chan
Date Deposited:	19 Oct 2021 17:04
Last Modified:	19 Oct 2021 17:04
URI:	https://norma.ncirl.ie/id/eprint/5115

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