Malware Classification using Km-SVM

Ghorpade, Ashish (2020) Malware Classification using Km-SVM. Masters thesis, Dublin, National College of Ireland.

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Abstract

Malware identification and classification is a problem faced even in this decade. This is majorly due to the fact that advance malware are more sophisticated in nature and have state of the art abilities to remain hidden or change their code/behaviour more like a smart malware. Hence, old detection and classification techniques are no longer as effective. This resulted in pivoting towards machine learning for better detection and classification of such malware. This is the motive behind the topic of this research thesis. Through this thesis, effort has been made to better classify malware using a combination of supervised and unsupervised machine learning while keeping the accuracy at acceptable levels and reducing training time. This led to conducting study not only in malware classification but also in other fields such as speech recognition and medical research to identify different techniques which could possibly be used for successful malware classification.

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 T Technology > T Technology (General) > Information Technology > Computer software 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:	Caoimhe Ní Mhaicín
Date Deposited:	02 Apr 2020 14:20
Last Modified:	02 Apr 2020 14:20
URI:	https://norma.ncirl.ie/id/eprint/4166

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