Detection of DDoS attacks in the IoT devices Using Machine Learning Models on Urban IoT Dataset

Obetta, Simon Onyebuchi (2022) Detection of DDoS attacks in the IoT devices Using Machine Learning Models on Urban IoT Dataset. Masters thesis, Dublin, National College of Ireland.

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Abstract

As the Internet of Things (IoT) has grown in popularity in recent years, attackers are increasingly targeting IoT environments to perform malicious attacks such as DDoS. This is due to the inadequate security implementation and management of IoT devices. Sometimes, the infected IoT devices can be used as bots by attackers to launch a DDoS attack on a target. Although various security methods have been introduced recently for IoT devices, an effective detection method is still required. This motivates the development of four machine learning models for DDoS detection. Using three modern neural network algorithms such as Feedforward Neural Network (FNN), Deep Neural Network (DNN), and Autoencoder and the conventional Random Forest for DDoS detection performance comparison. The detection system uses a public dataset, Urban IoT to detect attacks using these four algorithms. Experiment results show that DNN achieved the highest accuracy of 95.9%, while the Random Forest achieved the lowest accuracy of 94.2%.

Item Type:	Thesis (Masters)
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 Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	22 Dec 2022 13:55
Last Modified:	07 Mar 2023 12:42
URI:	https://norma.ncirl.ie/id/eprint/6033

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