By Topic

A Practical Taint-Based Malware Detection

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)
Xiao-song Zhang ; Comput. Sci. & Eng. Dept., Univ. of Electron. Sci. & Technol. of China, Chengdu ; Liu Zhi ; Da-peng Chen

Malware has posted a great risk to the privacy of users and data. Unfortunately, current anomaly detection is both coarse-grained and ineffective to detect them, because some behaviors can only be triggered in specific circumstances, moreover, it is difficult to analyze and evaluate compromised data. In this paper, we address the two problems via taint-based analysis. First, we precisely characterize malware behaviors in virtual environments, where behaviors can be completely explored by tainting return values of security-related system calls in order to traverse multiple execution branches. Second, sensitive data are also tainted to track their propagation to determine whether they are transmitted maliciously. A supporting distributed architecture is presented to optimize efficiency. Our approach is an effective complement to current anomaly-based models. The initial experiment demonstrates our system can detect a variety of malware with high accuracy and low overhead.

Published in:

Apperceiving Computing and Intelligence Analysis, 2008. ICACIA 2008. International Conference on

Date of Conference:

13-15 Dec. 2008