Article
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PDF Malware Detection using Machine learning
Version 1
: Received: 26 January 2023 / Approved: 30 January 2023 / Online: 30 January 2023 (12:55:47 CET)
How to cite: AlMahadeen, A.; alkasassbeh, M. PDF Malware Detection using Machine learning. Preprints 2023, 2023010557. https://doi.org/10.20944/preprints202301.0557.v1 AlMahadeen, A.; alkasassbeh, M. PDF Malware Detection using Machine learning. Preprints 2023, 2023010557. https://doi.org/10.20944/preprints202301.0557.v1
Abstract
Portable Document Format (PDF) is one of the most widely used files types worldwide in data exchange, this has encourage hackers to utilize such files to spread any malicious content through PDF, utilizing different methods and techniques to accomplish that, on the other hand, security researches kept trying to improve detection methods to cope up to the rapidly increasing number of malwares daily, one of the commonly used detection technique nowadays is by utilizing artificial intelligence and Machine learning classificat; thision to help detecting PDF Malwares, in this paper, we utilize machine learning classifier Random Forest on a newly released PDF Malware dataset CIC-Evasive-PDFMal2022 to achieve the main goal of detecting malicious PDF documents, results showing a detection accuracy of around 99.5%
Keywords
PDF; Malware; Machine Learning; Python; Random Forest
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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