Implementation of machine learning algorithms for advanced persistent threat detection and response

Ogunbanjo, Olamide Eniola (2023) Implementation of machine learning algorithms for advanced persistent threat detection and response. Masters thesis, Dublin, National College of Ireland.

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Abstract

The increasing prevalence of Advanced Persistent Threats (APTs) necessitates innovative detection and response mechanisms. This research delves into the application of machine learning algorithms for APT detection, addressing the challenges of concept drift and adversarial attacks. The primary aim is to assess and enhance machine learning's role in detecting and responding to APTs. A comprehensive review of current APT detection methodologies is presented, followed by the selection and implementation of specific machine learning algorithms on the BETH dataset. The study introduces a basic standalone Python script that preprocesses this dataset, facilitating the training and detection of APTs using the chosen algorithms. While the results demonstrate promising accuracy rates, the research acknowledges the script's foundational nature, suggesting avenues for future refinement and potential commercialisation. The significance of this study lies in bridging the gap between machine learning application and APT detection, offering a blueprint for cybersecurity professionals. The research not only contributes to the academic discourse on APT detection but also provides practical insights for organisations and individuals grappling with cybersecurity challenges.

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 > 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:	Tamara Malone
Date Deposited:	05 Nov 2024 10:58
Last Modified:	05 Nov 2024 10:58
URI:	https://norma.ncirl.ie/id/eprint/7142

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