DocumentCode :
2411018
Title :
Malware Type Recognition and Cyber Situational Awareness
Author :
Dube, Thomas ; Raines, Richard ; Peterson, Gilbert ; Bauer, Kenneth ; Grimaila, Michael ; Rogers, Steven
Author_Institution :
Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA
fYear :
2010
fDate :
20-22 Aug. 2010
Firstpage :
938
Lastpage :
943
Abstract :
Current technologies for computer network and host defense do not provide suitable information to support strategic and tactical decision making processes. Although pattern-based malware detection is an active research area, the additional context of the type of malware can improve cyber situational awareness. This additional context is an indicator of threat capability thus allowing organizations to assess information losses and focus response actions appropriately. Malware Type Recognition (MaTR) is a research initiative extending detection technologies to provide the additional context of malware types using only static heuristics. Test results with MaTR demonstrate over a 99% accurate detection rate and 59% test accuracy in malware typing.
Keywords :
computer networks; decision making; invasive software; pattern classification; computer network; cyber situational awareness; malware type recognition; tactical decision making processes; threat capability; Accuracy; Analysis of variance; Context; Decision trees; Feature extraction; Malware; Predictive models; Invasive software; machine learning; security;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Social Computing (SocialCom), 2010 IEEE Second International Conference on
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-8439-3
Electronic_ISBN :
978-0-7695-4211-9
Type :
conf
DOI :
10.1109/SocialCom.2010.139
Filename :
5591412
Link To Document :
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