Date of Award:
5-2014
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Dan Watson
Committee
Dan Watson
Committee
Ming Li
Committee
Nicholas Flann
Abstract
Illicit software that seeks to steal user information, deny service, or cause general mayhem on computer networks is often discovered after the damage has been done. The ability to discover network behavior of software before a computer network is utilized would allow administrators to protect and preserve valuable resources. Static reverse engineering is the process of discovering in a offline environment how a software application is built and how it will behave. By automating static reverse engineering, software behavior can be discovered before it is executed on client devices. Fingerprints are then built from the discovered behavior which is matched with known malicious fingerprints to identify potentially dangerous software.
Checksum
06523b845d96cbd2131caad499bb8d32
Recommended Citation
Sinema, Dan, "Automated Reverse Engineering of Malware to Develop Network Signatures to Match with Known Network Signatures" (2014). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 3315.
https://digitalcommons.usu.edu/etd/3315
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