By Topic

Malware Target Recognition of Unknown Threats

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Dube, T.E. ; Dept. of Electr. & Comput. Eng., U.S. Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA ; Raines, R.A. ; Grimaila, M.R. ; Bauer, K.W.
more authors

Organizations traditionally use signature-based commercial antivirus products as a frontline defense against malware, but advanced persistent threats craft custom malicious tools to achieve their objectives. Organizations safeguarding sensitive information have difficulty in identifying new malware threats among millions of benign executables using only signature-based antivirus systems. This paper extends a performance-based malware target recognition architecture that currently uses only static heuristic features. Experimental results show that this architectural component achieves an overall test accuracy of 98.5% against a malware set collected from operational environments, while three commercial antivirus products combine for a detection accuracy of only 60% with their most sensitive settings. Implementations of this architecture will enable organizations to self-discover new malware threats, providing enhanced situation awareness for cyberspace operators in hostile threat environments.

Published in:

Systems Journal, IEEE  (Volume:7 ,  Issue: 3 )