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Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning
Joint Authors
Meng, Qingkun
Feng, Chao
Zhang, Bin
Tang, Chaojing
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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 F1 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.
American Psychological Association (APA)
Meng, Qingkun& Feng, Chao& Zhang, Bin& Tang, Chaojing. 2017. Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1190718
Modern Language Association (MLA)
Meng, Qingkun…[et al.]. Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning. Mathematical Problems in Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1190718
American Medical Association (AMA)
Meng, Qingkun& Feng, Chao& Zhang, Bin& Tang, Chaojing. Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1190718
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190718