By Topic

Malware analysis using reverse engineering and data mining tools

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

3 Author(s)
Burji, S. ; Dept. of Comput. Sci., Univ. of Akron, Akron, OH, USA ; Liszka, K.J. ; Chan, C.

One challenge in malware analysis involves collecting useful data without risking experimenters' machines or systems. Static analysis of malware codebases is valuable in providing insights on malware development mechanisms, however, it cannot provide understanding in dynamic profiling of executable codes. In this paper, we present a case study of the well-known Nugache worm using existing reverse engineering tools to collect data from malwares running in a closed-lab environment. Useful dynamic patterns of malwares are generated by using a rough set based machine learning tool. The proposed approach can be used for the study of malware behaviors in a safe and pedagogical environment. The dynamic patterns generated by data mining tools may provide insights for specifying similarity measures used by network level Intrusion Detection Systems.

Published in:

System Science and Engineering (ICSSE), 2010 International Conference on

Date of Conference:

1-3 July 2010