DocumentCode
3418008
Title
Detecting metamorphic malware by using behavior-based aggregated signature
Author
Yanzhen Qu ; Hughes, Kit
Author_Institution
Sch. of Comput. Sci., Colorado Tech. Univ., Colorado Springs, CO, USA
fYear
2013
fDate
9-12 Dec. 2013
Firstpage
13
Lastpage
18
Abstract
The capability of advanced malware, such as metamorphic malware and polymorphic malware, are quickly outpacing our current abilities to detect their presence. For example, all current signature based malware-detection methods have become ineffective because they only create signatures based on the executable files that will be obfuscated by every instance of advanced malware. However, behavior is a characteristic in advanced malware that remains consistent throughout variants within a family of malware. We have capitalized upon this to create an aggregated signature for a family of malware. Creating a signature that spans a malware family allows it to identify known and new variants of that family. Using an aggregated signature versus many signatures for each variant for detection of malware provides many benefits to anti-virus vendors and the community as a whole by reducing the size of the signature database, reducing maintenance, and increasing speed of detection without losing accuracy.
Keywords
digital signatures; invasive software; advanced malware; antivirus vendors; behavior-based aggregated signature; metamorphic malware detection method; polymorphic malware; signature database size; Analytical models; Data models; Generators; Logistics; Malware; Software; aggregated signature; detection; metamorphic malware; polymorphic malware; signature;
fLanguage
English
Publisher
ieee
Conference_Titel
Internet Security (WorldCIS), 2013 World Congress on
Conference_Location
London
Type
conf
DOI
10.1109/WorldCIS.2013.6751010
Filename
6751010
Link To Document