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Opcode Frequency Based Malware Detection Using Hybrid Classifiers

Kollara, Arun Manoharan (2020) Opcode Frequency Based Malware Detection Using Hybrid Classifiers. Masters thesis, Dublin, National College of Ireland.

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Abstract

The world we live in is the world of technology. Almost every sector in business or organizations makes use of computers for storing information. Some information is private and sensitive to the users. It may include business ideas, government papers, bank account passwords etc. The malicious software is programmed by cyber-criminals to get a hold of this information. This can result in huge losses for the organization or an individual. There are traditional anti-viruses available in the market. However, the complexity and variety of malware are increasing day-by-day. They can bypass the traditional signature-based antimalware. Research has been focused on Machine learning to find a solution for this advanced malware. There are research conducted to detect a malware executable using opcodes. Opcodes are a part of machine level language that instructs the processor hardware what functions to perform. This thesis makes use of a machine learning-based hybrid algorithm to boost the accuracy of malicious file detection. The thesis developed makes use of opcodebased features.

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: 27 Jan 2021 16:25
Last Modified: 27 Jan 2021 16:25
URI: http://norma.ncirl.ie/id/eprint/4500

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