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A comparison of the classification of disparate malware collected in different time periods

Islam, Rafiqul, Tian, Ronghua, Moonsamy, Veelasha and Batten, Lynn 2011, A comparison of the classification of disparate malware collected in different time periods, in ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security Workshop, Deakin University School of Information Systems, Melbourne, Vic, pp. 22-27.

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Title A comparison of the classification of disparate malware collected in different time periods
Author(s) Islam, Rafiqul
Tian, Ronghua
Moonsamy, Veelasha
Batten, Lynn
Conference name Applications and Techniques in Information Security. Workshop (2nd : 2011 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 9 Nov. 2011
Title of proceedings ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security Workshop
Editor(s) Warren, Matthew
Publication date 2011
Conference series Applications and Techniques in Information Security Workshop
Start page 22
End page 27
Total pages 6
Publisher Deakin University School of Information Systems
Place of publication Melbourne, Vic
Keyword(s) malware
classification
static
dynamic
Summary It has been argued that an anti-virus strategy based on malware collected at a certain date, will not work at a later date because malware evolves rapidly and an anti-virus engine is faced with a completely new type of executable not as amenable to detection as the first was. In this paper, we test this idea by collecting two sets of malware, the first from 2002 to 2007, the second from 2009 to 2010 to determine how well the anti-virus strategy we developed based on the earlier set [14] will do on the later set. This anti-virus strategy integrates dynamic and static features extracted from the executables to classify malware by distinguishing between families. The resulting classification accuracies are very close for both datasets, with a difference of only 5.4%, the older malware being more accurately classified than the newer malware. This leads us to conjecture that current anti-virus strategies can indeed be modified to deal effectively with new malware.
Notes Reproduced with the kind permission of the copyright owner.
ISBN 9780987229809
Language eng
Field of Research 080303 Computer System Security
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2011
Copyright notice ©2011, Deakin University
Persistent URL http://hdl.handle.net/10536/DRO/DU:30045407

Document type: Conference Paper
Collections: School of Information Technology
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