基于数据挖掘的Web入侵检测  

Web Intrusion Detection Based on Data Mining

作　　者：尹淑玲[1] 龚鸣敏[1] 李蕾[1] 

机构地区：[1]武昌理工学院,湖北武汉430223

出　　处：《网络空间安全》2016年第8期66-67,74,共3页Cyberspace Security

基　　金：国家自然科学基金(41101412)

摘　　要：Web入侵检测系统对Web访问进行实时监控并能及时发现针对Web的攻击行为,能有效地解决Web所面临的安全问题。因为数据挖掘技术能从海量审计数据中挖掘出正常和异常行为模式,这不仅大量减少了人工分析和编码带来的繁重工作,也提高了入侵检测系统的适应性,因此,近年来在入侵检测领域大量用到数据挖掘技术。论文介绍了数据挖掘技术及其在Web入侵检测系统中的应用,设计了基于数据挖掘技术的Web入侵检测系统,能有效地阻止针对Web应用的异常入侵。Web intrusion detection system can monitor Web access transactions in real-time and detect attacks in time, which can solve the Web security problems effectively. Because data mining technology can mine normal and abnormal behavior model from vast amounts of audit data, not only reducing the heavy work of manual analysis and coding significantly, but also improving the adaptability of intrusion detection system. Data mining technology is used in the field of intrusion detection widely. Data mining technology and it＇s application in the Web intrusion detection are introduced here, and the Web intrusion detection system based on data mining is designed, which can effectively prevent the abnormal intrusion of the Web application.

关 键 词：WEB攻击 Web入侵检测 数据挖掘 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]