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Accurate Detection of Malicious Code in PDF Files using Machine Learning

Hassan Shivashankar, Kiran (2020) Accurate Detection of Malicious Code in PDF Files using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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The 21st century has been the age of technology. The continuous growth in technology has also forced growth in the field of cybersecurity. Cyber-criminal is continuously researching for new ways to perform cyber-crime. PDF files are very popular in the current age. Most of the official documents to learning materials are now been circulated and read in PDF format. Due to its high flexibility in features PDF format has become very popular. Due to these reasons, cyber-criminals are now been using PDF files to exploit systems and perform cybercrime. PDF file formats are being used by individuals who have no knowledge about computer security. So, they are very vulnerable to any form of PDF-based cyber-attack. This thesis aims at developing a system to detect malicious code in PDF files using machine learning algorithms. The thesis will be performing a comparative study of the algorithm to find the algorithm that gives the best results when it comes to PDF malware detection.
Keywords: PDF, Malware, Adaboost, Logistic Regression, Decision Tree.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 26 Jan 2021 15:51
Last Modified: 26 Jan 2021 15:51

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