Cart (Loading....) | Create Account
Close category search window

A virus detection scheme based on features of Control Flow Graph

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

1 Author(s)
Zongqu Zhao ; Sch. of Comput. Sci. & Technol., Henan Polytech. Univ., Jiaozuo, China

For the well-known reasons, the virus detection schemes based on signature manifest unsatisfactory performance when they dispose the previously unknown virus. Recently, machine learning methods were introduced to build new ways for virus detection. They adopted classification algorithms to learn patterns in the binary code files in order to classify unknown files. In this paper, we present a graph features based method, which can be used in the process of machine learning, and design a virus detection model based on our feature method. The features are extracted from Control Flow Graph (CFG) of executable. We follow a threefold research methodology in our detection model: (1) create the CFG of the executables, (2) extract features from the CFG and create training data, (3) generate classifiers according to specific machine learning algorithms, and detect virus with these classifiers. For the sake of fixed sum of features, our model avoids situation that too much features could be found in other feature methods and leaves the filter step out of it, so it presents the efficient and scalability. With our experiments, we were able to achieve as high as 95.9% detection rate and as low as 5.9% false positive rate on novel malware.

Published in:

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Date of Conference:

8-10 Aug. 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.