使用支持向量机和随机决策森林检测便携式文档格式文件(PDF)中的恶意软件

Abdachul Charim, Setio Basuki, Denar Regata Akbi
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引用次数: 1

摘要

可移植文档格式是一种非常强大的文件类型来传播恶意软件,因为它是许多人需要的,这使得PDF恶意软件不能掉以轻心。嵌入了恶意软件的PDF文件可以是Javascript、URL访问、感染了恶意软件的媒体等。具有多种预防措施有助于传播,例如在本研究中采用危险文件之间的分类方法。在以往的研究中,准确率最高的两种分类方法是支持向量机和随机森林。有500个数据集组成2类,即恶意和非恶意和21个恶意PDF特征作为分类过程的材料。通过计算混淆矩阵对两种方法的分类结果进行比较,结果表明随机森林方法的分类结果优于支持向量机方法,尽管其值仍然不完美。
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Detect Malware in Portable Document Format Files (PDF) Using Support Vector Machine and Random Decision Forest
Portable Document Format is a very powerful type of file to spread malware because it is needed by many people, this makes PDF malware not to be taken lightly. PDF files that have been embedded with malware can be Javascript, URL access, media that has been infected with malware, etc. With a variety of preventive measures can help to spread, for example in this study using the classification method between dangerous files or not. Two classification methods that have the highest accuracy value based on previous research are Support Vector Machine and Random Forest. There are 500 datasets consisting of 2 classes, namely malicious and not malicius and 21 malicius PDF features as material for the classification process. Based on the calculation of Confusion Matrix as a comparison of the results of the classification of the two methods, the results show that the Random Forest method has better results than Support Vector Machine even though its value is still not perfect.
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