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Vaidya, Vedant Mangesh (2024) Employing SVM and Random Forest for enhanced detection of Linux-based malware. Masters thesis, Dublin, National College of Ireland.
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Abstract
The Malware Detection System based on the state of art of machine learning algorithms of constructing the classifiers and aims to increase the level of cybersecurity by providing automated analysis and classification of the executable files based on the malware/benign model. This report details the system's design, implementation, and evaluation, focusing on two core machine learning models: Some of the commonly used methods are Support Vector Machine (SVM) and Random Forest. Finally, Grid Search was applied to perform the optimization of the SVM model which in turn has enhanced the malware detection acuity. The evaluation was carried out using ten new threats that the system did not detect previously; the SVM model found a new virus file that was not detected by the system previously. Thus, the findings of this work are useful for enhancing the field of cybersecurity due to the proved efficiency of the ML algorithms in responding to emerging threats and the detailed instructions on the configuration of the Malware Detection System.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Heffernan, Niall 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 Cyber Security |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 31 Jul 2025 13:14 |
| Last Modified: | 31 Jul 2025 13:14 |
| URI: | https://norma.ncirl.ie/id/eprint/8383 |
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