A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning
Joint Authors
Ren, Jiadong
Huaizhi, Yan
Zheng, Zhangqi
Liu, Qian
Wei, Zhiyao
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-03
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
Buffer overflow vulnerability is the most common and serious type of vulnerability in software today, as network security issues have become increasingly critical.
To alleviate the security threat, many vulnerability mining methods based on static and dynamic analysis have been developed.
However, the current analysis methods have problems regarding high computational time, low test efficiency, low accuracy, and low versatility.
This paper proposed a software buffer overflow vulnerability prediction method by using software metrics and a decision tree algorithm.
First, the software metrics were extracted from the software source code, and data from the dynamic data stream at the functional level was extracted by a data mining method.
Second, a model based on a decision tree algorithm was constructed to measure multiple types of buffer overflow vulnerabilities at the functional level.
Finally, the experimental results showed that our method ran in less time than SVM, Bayes, adaboost, and random forest algorithms and achieved 82.53% and 87.51% accuracy in two different data sets.
The method presented in this paper achieved the effect of accurately predicting software buffer overflow vulnerabilities in C/C++ and Java programs.
American Psychological Association (APA)
Ren, Jiadong& Zheng, Zhangqi& Liu, Qian& Wei, Zhiyao& Huaizhi, Yan. 2019. A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1210606
Modern Language Association (MLA)
Ren, Jiadong…[et al.]. A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning. Security and Communication Networks No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1210606
American Medical Association (AMA)
Ren, Jiadong& Zheng, Zhangqi& Liu, Qian& Wei, Zhiyao& Huaizhi, Yan. A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1210606
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210606