Network Intrusion Detection: A Cooperative Security in The IoT Ecosystem Using Ensemble ML Algorithm

Isedu, Mariam Uleyele (2024) Network Intrusion Detection: A Cooperative Security in The IoT Ecosystem Using Ensemble ML Algorithm. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rapid increase in the use of Internet of Things (IoT) devices across various sectors, industries and organizations has not only transformed the way we interact with the digital world, but it has also brought about notable security limitations. One of the major security drawbacks is the attack and spread of malwares like botnets that have the capabilities of been targeted towards DoS/DDoS attack, which can pose as a major threat to the confidentiality, integrity, availability and the entire security posture of any IoT ecosystems. This research paper presents a novel approach for DoS/DDoS attack detection in IoT ecosystem, employing a cooperative monitoring model designed in the form of a ‘fog node’ to prevent network intrusion. The proposed solution leverages a stacking ensemble machine learning technique, integrating the strengths of the Random Forest (RF) and Extreme Gradient Boost (XGBoost) algorithms as base models, and then subsequently restacked into a Random Forest model trained using the publicly available 'UNB CIC IoT 2023’ dataset. The cooperative security nature of the model ensures node to node security monitoring of network traffic and enabling real-time attack identification in the form of anomalies on the network. Experimental results and evaluations demonstrate the effectiveness of our proposed model's high performance in terms of accuracy, recall, precision, F1-score and high detection rate with low false positive rate compared to single-layer models. This research contributes to the field of cybersecurity by providing a proactive, efficient and effective method for enhancing IoT network resilience through advanced machine learning techniques and cooperative defense mechanisms.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Aleburu, Joel 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 Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	30 Jul 2025 08:49
Last Modified:	30 Jul 2025 08:49
URI:	https://norma.ncirl.ie/id/eprint/8320

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