A Feature Set of Small Size for the PDF Malware Detection

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2308.04704.pdf (554.65 KB)

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https://arxiv.org/abs/2308.04704

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https://doi.org/10.48550/arXiv.2308.04704
 http://hdl.handle.net/11603/29437

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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https://orcid.org/0000-0001-9494-7139

Date

2023-08-10

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conference papers and proceedings
postprints
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Attribution 4.0 International (CC BY 4.0)

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Abstract

Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as trustworthy data resources, and are accessible across different platforms. Therefore, researchers have developed many different PDF malware detection methods. Performance in detecting PDF malware is greatly influenced by feature selection. In this research, we propose a small features set that don’t require too much domain knowledge of the PDF file. We evaluate proposed features with six different machine learning models. We report the best accuracy of 99.75% when using Random Forest model. Our proposed feature set, which consists of just 12 features, is one of the most conciseness in the field of PDF malware detection. Despite its modest size, we obtain comparable results to state-of-the-art that employ a much larger set of features.