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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 5452396, 13 pages
Research Article

Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

School of Electronic Science and Engineering, National University of Defense Technology (NUDT), Changsha, Hunan, China

Correspondence should be addressed to Chao Feng

Received 1 July 2017; Revised 10 October 2017; Accepted 27 November 2017; Published 21 December 2017

Academic Editor: Nazrul Islam

Copyright © 2017 Qingkun Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Buffer overflow vulnerability is a kind of consequence in which programmers’ intentions are not implemented correctly. In this paper, a static analysis method based on machine learning is proposed to assist in auditing buffer overflow vulnerabilities. First, an extended code property graph is constructed from the source code to extract seven kinds of static attributes, which are used to describe buffer properties. After embedding these attributes into a vector space, five frequently used machine learning algorithms are employed to classify the functions into suspicious vulnerable functions and secure ones. The five classifiers reached an average recall of 83.5%, average true negative rate of 85.9%, a best recall of 96.6%, and a best true negative rate of 91.4%. Due to the imbalance of the training samples, the average precision of the classifiers is 68.9% and the average score is 75.2%. When the classifiers were applied to a new program, our method could reduce the false positive to 1/12 compared to Flawfinder.