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.
2010 IEEE Second International Conference on Social Computing. Session: "Mission Assurance: Tools, Techniques, and Methodologies"
T. Dube, R. Raines, G. Peterson, K. Bauer, M. Grimaila and S. Rogers, "Malware Type Recognition and Cyber Situational Awareness," 2010 IEEE Second International Conference on Social Computing, Minneapolis, MN, USA, 2010, pp. 938-943, doi: 10.1109/SocialCom.2010.139.