Test Case Generation for Vulnerability Detection Using Genetic Algorithm

DOI

10.2991/nceece-15.2016.213How to use a DOI?

Keywords

genetic algorithm; fuzz; path coverage; test cost

Abstract

In order to elevate efficiency of traditional Fuzzing technique, a novel method using genetic algorithm is proposed based on path coverage and test cost. There are evidences that GA has been already successful in generating test cases. Considering path coverage as the test adequacy criterion, we have designed a GA-based test data generator that is able to synthesize multiple test data to cover multiple target paths. Meanwhile, in order to reduce the test cost in Fuzzing process, test cost is analyzed respectively from running time and loop structure in the method. Experimental results show that proposed approach could obtain higher vulnerability detection accuracy and efficiency.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Shuai
AU  - Haifeng Li
AU  - Jian Wang
AU  - Quan Zhang
AU  - ChaoJing Tang
PY  - 2015/12
DA  - 2015/12
TI  - Test Case Generation for Vulnerability Detection Using Genetic Algorithm
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 1198
EP  - 1203
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.213
DO  - 10.2991/nceece-15.2016.213
ID  - Shuai2015/12
ER  -