Leveraging Data Analytics to enhance Cybersecurity Threat Detection

Rajendran, Anantha Padmanabha (2025) Leveraging Data Analytics to enhance Cybersecurity Threat Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research explores integrating data analytics in cybersecurity systems to improve threat detection and response. The project pits the world of data-driven approaches against routine security methodologies. The Decision Tree, K-Means Clustering, and Neural Network algorithms tend to suggest that the supervised model of learning, especially the Neural Network, is better at detecting and adapting to threats with better accuracy. The unsupervised methods did not fare well, stressing the need to enhance real-time and data-driven security technologies. It then suggests that robustness needs to be built through more advanced analytics to push for the next-generation cybersecurity solutions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Anu UNSPECIFIED
Uncontrolled Keywords:	CNN (Convolutional Neural Network); F1-Score; LIME (Local Interpretable Model-agnostic Explanations); LSTM (Long Short-Term Memory); SHAP (SHapley Additive exPlanations); Silhouette Score; Z-Score Normalization
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 Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	18 Nov 2025 17:40
Last Modified:	18 Nov 2025 17:40
URI:	https://norma.ncirl.ie/id/eprint/8944

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