A Feature Set of Small Size for the PDF Malware Detection
| dc.contributor.author | Liu, Ran | |
| dc.contributor.author | Nicholas, Charles | |
| dc.date.accessioned | 2023-08-30T15:18:30Z | |
| dc.date.available | 2023-08-30T15:18:30Z | |
| dc.date.issued | 2023-08-10 | |
| dc.description | 29TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, August 6-10, 2023 | en_US |
| dc.description.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. | en_US |
| dc.description.uri | https://arxiv.org/abs/2308.04704 | en_US |
| dc.format.extent | 6 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | postprints | en_US |
| dc.identifier | doi:10.13016/m2oqdr-fd9z | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.2308.04704 | |
| dc.identifier.uri | http://hdl.handle.net/11603/29437 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.rights | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | A Feature Set of Small Size for the PDF Malware Detection | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0001-9494-7139 | en_US |