Malware Detection in PDF and PE Files Using Machine Learning and Feature Selection

Usoroh, Rosemary Uwem, Ghergulescu, Ioana and Moldovan, Arghir-Nicolae (2025) Malware Detection in PDF and PE Files Using Machine Learning and Feature Selection. In: 2025 13th International Symposium on Digital Forensics and Security (ISDFS). IEEE, Boston, MA, USA. ISBN 979-833150993-4

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Abstract

Malware detection is a crucial task in cybersecurity. Due to the dynamic nature of malware and the presence of new variants, signature-based malware detection solutions must be complemented by AI and machine learning-based solutions. Malware can hide in different file formats, and use obfuscation techniques to bypass detection. This paper contributes to the body of research by investigating the use of machine learning algorithms and feature selection for the detection of malware in Portable Executable (PE) and Portable Document Format (PDF) files. Different machine learning (ML) algorithms were used. The results from the experiments showed that RF outperformed other algorithms in terms of accuracy, F1 and AUC. Moreover, feature selection can decrease significantly the model building time while maintaining high prediction performance.

Item Type:	Book Section
Uncontrolled Keywords:	malware detection; machine learning; feature selection; ML in cybersecurity
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 > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	07 Jul 2025 10:24
Last Modified:	07 Jul 2025 10:24
URI:	https://norma.ncirl.ie/id/eprint/8065

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