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Vittal Rao, Naveen Rao (2023) Using Machine Learning in Intrusion Detection System to Improve Model Efficiency and Reduce Training Time using Different Feature Selection Methods and Classifiers. Masters thesis, Dublin, National College of Ireland.
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Abstract
This study aims to address the critical need of enhancing Intrusion Detection Systems (IDS) by the strategic application of Machine Learning (ML). The study's main objective is to shorten training times and boost model effectiveness by utilizing a range of feature selection techniques and classifiers. The comprehensive experience includes the creation of machine learning models, astute visualization, and painstaking data preprocessing. Significant findings reveal the ongoing dominance of the Random Forest model and provide insight into the subtle differences in the effects of various feature selection techniques on various classifiers. The study presents a possible avenue for bolstering cybersecurity frameworks by providing useful insights into the adaptability and robustness of machine learning in the context of intrusion detection systems.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Hafeez, Taimur 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 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: | 26 May 2025 09:30 |
| Last Modified: | 26 May 2025 09:30 |
| URI: | https://norma.ncirl.ie/id/eprint/7643 |
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