Browse > Article
http://dx.doi.org/10.5762/KAIS.2019.20.6.541

A Study on Hybrid Fuzzing using Dynamic Analysis for Automatic Binary Vulnerability Detection  

Kim, Taeeun (Korea Internet & Security Agency)
Jurn, Jeesoo (Korea Internet & Security Agency)
Jung, Yong Hoon (Dept. of Computer Science, SoongSil University)
Jun, Moon-Seog (Dept. of Computer Science, SoongSil University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.6, 2019 , pp. 541-547 More about this Journal
Abstract
Recent developments in hacking technology are continuing to increase the number of new security vulnerabilities. Approximately 80,000 new vulnerabilities have been registered in the Common Vulnerability Enumeration (CVE) database, which is a representative vulnerability database, from 2010 to 2015, and the trend is gradually increasing in recent years. While security vulnerabilities are growing at a rapid pace, responses to security vulnerabilities are slow to respond because they rely on manual analysis. To solve this problem, there is a need for a technology that can automatically detect and patch security vulnerabilities and respond to security vulnerabilities in advance. In this paper, we propose the technology to extract the features of the vulnerability-discovery target binary through complexity analysis, and select a vulnerability-discovery strategy suitable for the feature and automatically explore the vulnerability. The proposed technology was compared to the AFL, ANGR, and Driller tools, with about 6% improvement in code coverage, about 2.4 times increase in crash count, and about 11% improvement in crash incidence.
Keywords
Software Vulnerability; Vulnerability Analysis; Fuzzing; Symbolic Execution; Binary;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S.H. Oh, T.E. Kim, H.W. Kim, "Technology Analysis on Automatic Detection and Defense of SW Vulnerabilities", Journal of the Korea Academia-Industrial cooperation Society, Vol. 18, No. 11, pp. 94-103, 2017. DOI: https://doi.org/10.5762/KAIS.2017.18.11.94   DOI
2 Defense Advanced Research Projects Agency(DARPA), Program, DARPA, c2016, From: https://www.darpa.mil/program/cyber-grand-challenge, (accessed Oct., 11, 2017).
3 Miller, B.P.; Fredriksen, L.; So, B. "An empirical study of the reliability of UNIX utilities", Commun. ACM 1990, 33, 32.44.   DOI
4 Bekrar, S.; Bekrar, C.; Groz, R.; Mounier, L. "A taint based approach for smart fuzzing". In Proceedings of the IEEE Fifth International Conference on Software Testing, Verification and Validation, Montreal, QC, Canada, 17-21 April 2012; pp. 818-825.
5 American Fuzzy Lop. Available online: http://lcamtuf.coredump.cx/afl/ (accessed April 30, 2018).
6 King, J.C. "Symbolic execution and program testing". Commun. ACM 1976, 19, 385-394.   DOI
7 Cha, S.K.; Avgerinos, T.; Rebert, A.; Brumley, "D. Unleashing mayhem on binary code". In Proceedings of the IEEE Symposium on Security and Privacy, San Francisco, CA, USA, 20-23 May 2012; pp. 380-394.
8 Stephens, N.; Grosen, J.; Salls, C.; Dutcher, A.;Wang, R.; Corbetta, J.; Shoshitaishvili, Y.; Kruegel, C.; Vigna, G. "Driller: Augmenting Fuzzing through Selective Symbolic Execution". NDSS 2016, 16, 1-16.
9 U.S. National Vulnerability Database. Available online: http://cve.mitre.org/cve/ (accessed April 30, 2019).