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

页码：1911 / 1916

页数：6

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