Minimising False Positives in SOC using AI/ML

Sreekumar, Kiran (2025) Minimising False Positives in SOC using AI/ML. Masters thesis, Dublin, National College of Ireland.

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Abstract

This dissertation reviews the application of current artificial intelligence and machine learning (AI/ML) models in reducing false positives in Security Operations Centers (SOCs). The paper features a cybersecurity dataset as a real-world problem, and preprocessing was standardized through its implementation on a real-life cybersecurity dataset, utilizing two models: Random Forest and Gradient Boosting. The most commonly used metrics that will be used to test these models include accuracy, precision, recall, and F1-score. It is clear in the findings that both models were good; Gradient Boosting has a bit better true and false positive balance. The study accords with the existing literature, and it has an applied and scholarly direction of understanding how to improve alert sensitivity in the SOC settings. It also provides a list of shortcomings and suggests plans for improvement using powerful models, real-time processing, and explainable AI in order to aid the decision-making of the analysts.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Prior, Michael UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence 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:	19 Aug 2026 15:15
Last Modified:	19 Aug 2026 15:15
URI:	https://norma.ncirl.ie/id/eprint/9552

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