Analysing The Effectiveness of Machine Learning Algorithms in Intrusion Detection Systems for IoT Networks

Mylavarapu, Sandeep Kumar (2024) Analysing The Effectiveness of Machine Learning Algorithms in Intrusion Detection Systems for IoT Networks. Masters thesis, Dublin, National College of Ireland.

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Abstract

Network Intrusion Systems are very essential in managing networks and mainly ensure to keep their security information safe. Conventional approaches based on rule analysis have mostly failed to provide effective solutions for complicated attacks by learning from the outcomes of IDS implementing in complex environments like cloud computing and IoT. This work examines Random Forest, Support Vector Machine and KNearest Neighbour ML algorithms for IDS using the NSL- Knowledge Discovery in Databases. The analysis of these algorithms which considers the classification accuracy and estimations by using the measures of precision, recall, F1 scores and random forest that results the best outcome with an overall prediction accuracy followed by KNN with 97.45 Percent and SVM with 95.54 Percent. The findings of the study that discuss about the feature selection, data preprocessing and the model evaluation as critical areas that are important to the enhanced performance of IDS. Moreover problems like lack of balance of datasets, the problems that arise due to big data and high computational complexity in real-life networks are still open to be solved. To overcome these types of stated limitations the study which effectively suggests to use oversampling methods such as SMOTE, hyperparameter tuning and the integration of deep learning to improve the process of anomaly identification. For future studies it is recommended to work with efficient real type datasets, together with ensemble solutions and employ ML IDS in real-world systems which comprise IoT and cloud networks to enhance performance flexibility and security.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	16 Jul 2025 08:40
Last Modified:	16 Jul 2025 08:40
URI:	https://norma.ncirl.ie/id/eprint/8128

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