ML Advancements in Malware Detection: Bridging Memory and Behavior

Oad, Ajay Kumar (2023) ML Advancements in Malware Detection: Bridging Memory and Behavior. Masters thesis, Dublin, National College of Ireland.

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Abstract

Malware remains a persistent and evolving challenge in the digital landscape, often evading detection by traditional security measures. As new malware types emerge with increasing frequency, it is crucial to develop effective tools and techniques to combat these threats. This study investigates the potential of advanced machine learning (ML) techniques to enhance the detection and classification of malicious software. Employing a variety of ML models on three distinct datasets, we conducted AV detection on each dataset to assess its effectiveness in identifying and classifying malware within the datasets. Our findings suggest that hybrid detection models, which combine both memory and behavioral features, hold the most promise for improving malware detection accuracy and adaptability. We discovered that using both memory and behavioral features significantly increased detection accuracy from 73.72% to 81.02%, highlighting the effectiveness of ML models in detecting malware. Our next step is to investigate the stability of these methods when incorporating specific data features or employing optimization techniques.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir-Nicolae 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 > 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:	21 Apr 2025 11:46
Last Modified:	21 Apr 2025 11:46
URI:	https://norma.ncirl.ie/id/eprint/7451

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