Streamlining Personalised Vulnerability Analysis and Anomaly Detection through API requests with a Random Forest Model

Thiyada Sridharan, Athipan (2023) Streamlining Personalised Vulnerability Analysis and Anomaly Detection through API requests with a Random Forest Model. Masters thesis, Dublin, National College of Ireland.

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Abstract

While cyber threats are rapidly evolving, it is crucial to have effective anomaly detection systems to monitor our server. This research introduces a machine learning-based detector designed to identify anomalies and vulnerability analysis in web applications. By analyzing historical data of known vulnerabilities and monitoring real-time trac, the system distinguishes between normal and malicious activities, ensuring a strong security defence. Integrating this model into a web application allows for an automated, immediate response to potential security threats. The study’s findings suggest that applying machine learning algorithms can significantly decrease the likelihood of overlooking security breaches, thus strengthening web applications against evolving cyber-attacks.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Vangujar, Apurva UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > Z719 Libraries (General) Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms 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:	25 Apr 2025 12:44
Last Modified:	25 Apr 2025 12:44
URI:	https://norma.ncirl.ie/id/eprint/7472

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