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Marath, Ashwathy Ajaykumar (2024) Comparing the Capabilities of Ensemble Learning Algorithms and SAST Tools for Effective Code Based Vulnerability Detection. Masters thesis, Dublin, National College of Ireland.
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Abstract
Based on the VUDENC and DiverseVul benchmarks, this work evaluates ensemble learning algorithms and SAST tools for software vulnerability detection. Secondary qualitative research data was collected between 2016 and 2024, and quantitative experiments were employed. For handling class imbalance, both Random Forest, XGBoost, LightGBM, and CatBoost ensemble models were experimented on with and without SMOTE. Ensemble models perform better than SAST techniques with XGBoost having the highest ROC-AUC score of 0.76 and Random Forest having stable majority class accuracy. SAST tools were okay for level L concerns but had higher levels of false positives and lower precision. Hybrid techniques can be used in the future to minimize false alarms and enhance immunity to software attacks in ensemble models.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Moldovan, Arghir Nicolae 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: | 23 Jul 2025 14:50 |
| Last Modified: | 23 Jul 2025 14:50 |
| URI: | https://norma.ncirl.ie/id/eprint/8223 |
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