Enhancing Cloud Security in the Financial Sector: An AI-Driven Threat Detection and Response Framework

Dammu, Srinivas (2024) Enhancing Cloud Security in the Financial Sector: An AI-Driven Threat Detection and Response Framework. Masters thesis, Dublin, National College of Ireland.

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Abstract

The growing number of cyber-attacks targeting the financial sector because of expanding digital payment solutions requires improved security defenses. Researchers introduce a dual-level Intrusion Detection System (IDS) specifically tailored for protecting the cloud framework of financial industry operations. The system integrates a K-Nearest Neighbors (KNN) model for binary classification, achieving 92.79% accuracy in detecting benign and malicious traffic, and a Deep Neural Network (DNN) for multiclass classification, reaching 94.19% accuracy in identifying seven attack types: Bot, DDoS-HOIC, DoS-Hulk, SSH-Bruteforce, DoS-GoldenEye, Infiltration, and DDoS-LOIC-HTTP. The research utilized the CICIDS2018 dataset for both training machines and testing performances while preprocessing procedures such as feature selection together with scaling and balanced sampling resulted in optimal system outcomes. Our system delivers powerful security while maintaining visibility through a simple interface that tracks live threats and displays transaction progress along with attack information. The proposed solutions in this research extend Intrusion Detection Systems (IDS) to tackle essential cybersecurity threats existing within financial cloud network environments.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mustafa, Raza Ul UNSPECIFIED
Subjects:	H Social Sciences > HG Finance 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
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	18 Jul 2025 10:43
Last Modified:	18 Jul 2025 10:43
URI:	https://norma.ncirl.ie/id/eprint/8198

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