Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680235
Title: Detection of obfuscated malware
Author: O'Kane, P. C.
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2014
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Abstract:
A cyber war exists between anti-malware researchers and malware writers. At the heart of this war rages a weapons race that has existed for decades, originating the 19805, with the arrival of the first computer virus. Obfuscation is one of the latest strategies employed by malware writers to camouflage the tell-tale signs of malware and thereby undermine anti-malware software making malware analysis difficult for anti-malware researchers.The the motivation for this research is, therefore, to find a malware detection strategy that is immune to the obfuscation methods used by the malware writers. One approach is to use program run-time traces (dynamic analysis) to perform N~gram analysis. N-gram analysis is the investigation of a program structure using bytes, charactersor text strings. The research presented in this thesis uses dynamic analysis to investigate malwaredetection using a Support Vector Machine (SVM) approach based on N-gram analysis. The key challenges addressed in this research are: Configuration of a host environment that can trace both benign and malicious software programs; SVM configuration using cross~validation to provide a robust classifier; the challenge of feature selection and feature reduction is addressed by first applying a feature filter and then presenting the reduced feature set to the SVM for feature selection. Several filtering methods are investigated and the findings have identified a suitable filter based on Eigenvectors. The final challenge associated with dynamic analysis is the length of time a program has to be run to ensure a correct classification. This is addressed in this research by investigating 14 different program run-lengths The findings show that obfuscated (packed and polymorphic) malware can be detected using a Support Vector Machine classifier with features extracted from program run-length traces.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.680235  DOI: Not available
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