NDLI: Auditing Buffer Overflow Vulnerabilities Using Hybrid Static-Dynamic Analysis

Content Provider	IEEE Xplore Digital Library
Author	Padmanabhuni, B.M. Hee Beng Kuan Tan
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Padmanabhuni, B.M.; Hee Beng Kuan Tan)
Abstract	Despite being studied for more than two decades buffer overflow vulnerabilities are still frequently reported in programs. In this paper, we propose a hybrid approach that combines static and dynamic program analysis to audit buffer overflows. Using simple rules, test data are generated to automatically confirm some of the vulnerabilities through dynamic analysis and the remaining cases are predicted by mining static code attributes. Confirmed cases can be directly fixed without further verification whereas predicted cases need to be manually reviewed to confirm existence of vulnerabilities. Since our approach combines the strengths of static and dynamic analyses, it results in an overall accuracy improvement. In our evaluation of approach using the standard benchmark suite, our classifiers achieved a recall over 92% and precision greater than 81%. The dynamic analysis component confirmed 51% of known vulnerabilities along with reporting 2 new bugs, thereby reducing by half, otherwise needed manual auditing effort.
Sponsorship	IEEE Comput. Soc.
Starting Page	394
Ending Page	399
File Size	278482
Page Count	6
File Format	PDF
ISBN	9781479935758
ISSN	07303157
DOI	10.1109/COMPSAC.2014.62
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-21
Publisher Place	Sweden
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Buffer overflows Data mining Arrays Input variables Accuracy Benchmark testing Predictive models static and dynamic analysis Vulnerability auditing buffer overflow data mining static code attributes input validation
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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