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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Detection of malicious PDF-files using machine learning algorithms
Creators: name: ÐÐ±ÑÐ»ÑÐµÐ², Ð Ð°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
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decision trees

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description: The topic of the graduate qualification work is Â«Detection of malicious PDF files using machine learning algorithmsÂ». The purpose of the study is increasing efficiency of malicious PDF files detection using machine learning algorithms. The subject of the work is models which build to detect malicious PDF files. The research set the following goals: Analysis of structure and working mechanisms of malicious PDF files. Applicability analysis of machine learning algorithms to malicious PDF files detection. Dataset building for research purposes. Malicious PDF files detection prototype software development using machine learning algorithms. Measuring the efficiency of malicious PDF files detection using machine learning algorithms. During the work analysis of structure and working mechanisms of malicious PDF files was done. Applicability analysis of machine learning algorithms to malicious PDF files detection was made and dataset for research purposes was build. The work resulted in development of the prototype which used for malicious PDF files detection and his efficiency was demonstrated. The conclusion that random forest algorithm is a good fit for malicious PDF files detection was made.