NDLI: Automatic software vulnerability detection based on guided deep fuzzing

Content Provider	IEEE Xplore Digital Library
Author	Jun Cai Shangfei Yang Jinquan Men Jun He
Copyright Year	2014
Description	Author affiliation: 61541 Troops, PLA, Beijing, China (Jinquan Men) \|\| Acad. of Equip., Beijing, China (Jun Cai; Jun He) \|\| Naval Acad. of Armament, Beijing, China (Shangfei Yang)
Abstract	Software security has become a very import part of information security in recent years. Fuzzing has proven successful in finding software vulnerabilities which are one major cause of information security incidents. However, the efficiency of traditional fuzz testing tools is usually very poor due to the blindness of test generation. In this paper, we present Sword, an automatic fuzzing system for software vulnerability detection, which combines fuzzing with symbolic execution and taint analysis techniques to tackle the above problem. Sword first uses symbolic execution to collect program execution paths and their corresponding constrains, then uses taint analysis to check these paths, the most dangerous paths which most likely lead to vulnerabilities will be further deep fuzzed. Thus, with the guidance of symbolic execution and taint analysis, Sword generates test cases most likely to trigger potential vulnerabilities lying deep in applications.
Starting Page	231
Ending Page	234
File Size	337153
Page Count	4
File Format	PDF
ISBN	9781479932788
ISSN	23270594
e-ISBN	9781479932795
DOI	10.1109/ICSESS.2014.6933551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software testing Databases Information security Fuzzing Taint analysis Software Symbolic execution Engines Monitoring Software vulnerability detection
Content Type	Text
Resource Type	Article

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