Research on Fuzzy Test Technology Based on Generative Adversarial Networks

被引：0
|
作者：
Liu, Yaoyang [1 ,2 ]
Wu, Bin [1 ,2 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Cyberspace Secur, Beijing, Peoples R China
[2] Beijing Univ Posts & Telecommun, Natl Disaster Recovery Technol Engn Lab, Beijing, Peoples R China
来源：
2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020) | 2020年
关键词：
Web Fuzzy test; GAN; XSS; Test Case;
D O I：
10.1109/ICMCCE51767.2020.00289
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fuzzy testing technology has a good effect in discovering web vulnerabilities, which is incomparable with other security testing technologies. Because of the randomness and unpredictability of its test cases, it can dig out the relevant vulnerabilities comprehensively and systematically. Before that, some researchers proposed to use genetic algorithm to generate test cases to solve the problem of single test cases. However, the test cases generated by genetic algorithm are often of high randomness, and the proportion of effective test cases is relatively low. According to this problem, I proposed to generate test cases based on generative countermeasure network. On the basis of ensuring its randomness, the relevant test cases are generated pertinently. Through relevant experiments, it is proved that the generated confrontation network can generate effective test cases. Compared with the test cases generated by genetic algorithm, its effectiveness is better and more web vulnerabilities can be found.
引用
收藏
页码：1316 / 1319
页数：4
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