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Varma, Dinal Sunil (2024) Zero-Day Exploit Identification in Web Application Using Machine Learning. Masters thesis, Dublin, National College of Ireland.
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Abstract
Zero day threats are a significant risk to web applications since they are new and exotic targets that they cannot be covered by most present day anti-hacking measures. This research aims at detecting Zero-day threats through combining machine learning algorithms with AWS CloudWatch logs improving a real time anomaly detection in cloud environment. The research employs a novel end to end machine learning workflow which helps in examining network traffic data for signs of drifts indicative of potential future zero-day attacks. Other types of models like Random Forest, Isolation Forest, Gradient Boost, Support Vector Classifier (SVC) and Deep Neural Network were tested for their performance efficiency where Deep Neural Network outperformed the other models with good the detection accuracy and five times fewer false positives. The factor of real-time alerting included effective mechanisms that have made it easier to alert user and respond quickly to any threat. Through AWS’s logging and high computational capabilities organizations can enhance their protection against advanced attacks with improved overall performance of cloud-based systems.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Jayasekera, Evgeniia 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: | 28 Jul 2025 15:24 |
| Last Modified: | 28 Jul 2025 15:24 |
| URI: | https://norma.ncirl.ie/id/eprint/8278 |
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