An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors

被引：0
|
作者：
Ren, Kunlun [1 ,3 ]
Qiang, Weizhong [1 ,3 ,4 ]
Wu, Yueming [2 ]
Zhou, Yi [1 ,3 ]
Zou, Deqing [1 ,3 ,4 ]
Jin, Hai [1 ,4 ,5 ]
机构：
[1] Huazhong University of Science and Technology, Wuhan, China
[2] Nanyang Technological University, Singapore
[3] National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Hust, Wuhan,430074, China
[4] Jinyinhu Laboratory, Wuhan, China
[5] National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Hust, Wuhan,430074, China
来源：
ISSTA 2023 - Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis | 2023年
关键词：
Engineering Village;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Empirical studies - Javascript obfuscations - Machine-learning - Malicious behavior - Malicious javascript - Malicious javascript detector - Manual identification - Training sets - Web attacks - WEB security
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页码：1420 / 1432
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