Web Behavior Detection Based on Deep Neural Network

被引：8
|
作者：
Yong, Binbin [1 ,2 ]
Liu, Xin [1 ]
Liu, Yan [3 ]
Yin, Hang [1 ]
Huang, Liang [2 ]
Zhou, Qingguo [1 ]
机构：
[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China
[2] Lanzhou Univ, Sch Phys Sci & Technol, Lanzhou, Gansu, Peoples R China
[3] Baidu, Baidu X Lab, Beijing, Peoples R China
来源：
2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) | 2018年
基金：
中国国家自然科学基金;
关键词：
network security; web injection; deep neural network; webshell detection; RANDOM FOREST; CLASSIFICATION;
D O I：
10.1109/SmartWorld.2018.00320
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Network security is a complex and difficult problem, in which web injection is one of the most serious security problems, especially webshell. A webshell is a malicious script used by an attacker with the intent to escalate and maintain persistent access to an already compromised Web application. To recognize the webshells, a tool to analyze the behavior of webshells is very important. In this paper, we design a deep neural network (DNN) and apply it into webshell detection. Results show that DNN has great potential for network security detection.
引用
收藏
页码：1911 / 1916
页数：6
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