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Fast automated unpacking and classification of malware

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thesis
posted on 06.12.2017, 00:00 by Silvio CesareSilvio Cesare
"Malware is a pervasive problem in distributed computer and network systems. Identification of malware variants provides great benefit in early detection. Control flow has been proposed as a characteristic that can be identified across variants, resulting in classificaiton empoying flowgraph based signatures. Static analysis is widely used to construct the signatures but can be ineffective if malware undergoes a code packing transforrmation to hide its real content. This thesis proposes a novel system, names Malwise, for malware classification using a fast application level emulator to reverse the code packing transformation, and two flowgraph matching algorithms to perform classification: exact flowgraph matching and approximate flowgraph matching"--Abstract.

History

Location

Central Queensland University

Additional Rights

I hereby grant to Central Queensland University or its agents the right to archive and to make available my thesis or dissertation in whole or in part through Central Queensland University’s Institutional Repository, ACQUIRE, in all forms of media, now or hereafter known. I retain all copyright, including the right to use future works (such as articles or books), all or part of this thesis or dissertation.

Open Access

Yes

External Author Affiliations

Faculty of Arts, Business, Informatics and Education;

Era Eligible

No

Supervisor

Dr Yang Xiang

Thesis Type

Master's by Research Thesis