HTTP-sCAN:Detecting HTTP-Flooding Attack by Modeling Multi-Features of Web Browsing Behavior from Noisy Web-Logs

WANG Jin; ZHANG Min; YANG Xiaolong; LONG Keping; XU Jie

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HTTP-sCAN:Detecting HTTP-Flooding Attack by Modeling Multi-Features of Web Browsing Behavior from Noisy Web-Logs

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HTTP-flooding attack disables the victimized web server by sending a large number of HTTP Get requests.Recent research tends to detect HTTP-flooding with the anomaly-based approaches,which detect the HTTP-flooding by modeling the behavior of normal web surfers.However,most of the existing anomaly-based detection approaches usually cannot filter the web-crawling traces from unknown searching bots mixed in normal web browsing logs.These web-crawling traces can bias the base-line profile of anomaly-based schemes in their training phase,and further degrade their detection performance.This paper proposes a novel web-crawling tracestolerated method to build baseline profile,and designs a new anomaly-based HTTP-flooding detection scheme(abbr.HTTP-sCAN).The simulation results show that HTTP-sCAN is immune to the interferences of unknown webcrawling traces,and can detect all HTTPflooding attacks.

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来源
《中国通信：英文版》 |2015年第2期|118-128|共11页
作者
WANG Jin; ZHANG Min; YANG Xiaolong; LONG Keping; XU Jie;
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作者单位

School of Computer and Communications Engineering;

University of Science and Technology Beijing;

Network Center of Chengdu University;

Research Center for Optical Internet & Mobile Information Network;

School of Communication & Information Engineer;

University of Electronic Science & Technology of China;

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原文格式 PDF
正文语种 chi
中图分类计算机网络;
关键词
Flooding攻击; Web服务器; 检测性能; HTTP; 网络日志; 浏览行为; 扫描; flooding攻击;

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HTTP-sCAN:Detecting HTTP-Flooding Attack by Modeling Multi-Features of Web Browsing Behavior from Noisy Web-Logs

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