J4 ›› 2009, Vol. 47 ›› Issue (6): 1264-1270.

• 计算机 • 上一篇    下一篇

基于异常检测的入侵检测技术

胡亮1, 金刚1, 于漫2, 任斐1, 任维武1   

  1. 1. 吉林大学 计算机科学与技术学院, 长春130012; 2. 长春工业大学 计算机科学与工程学院, 长春 130012
  • 收稿日期:2008-12-06 出版日期:2009-11-26 发布日期:2010-01-07
  • 通讯作者: 金刚 E-mail:jingang_jlu@126.com

Techniques of IDS Based on Anomaly Detection

HU Liang1, JIN Gang1, YU Man2, REN Fei1, REN Weiwu1   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. College of Computer Science and Technology, Changchun University of Technology, Changchun 130012, China
  • Received:2008-12-06 Online:2009-11-26 Published:2010-01-07
  • Contact: JIN Gang E-mail:jingang_jlu@126.com

摘要:

对目前的异常检测技术进行了全面概述, 按照采用的不同技术将异常检测分为基于统计、 基于机器学习和基于数据挖掘3种, 阐述了各种异常检测技术的特征, 并描述了目前基于异常入侵检测系统用到的各种算法及其实现方法. 通过实验结果, 比较了各种算法的检测效果.

关键词: 异常检测; 机器学习; 统计异常检测; 数据挖掘

Abstract:

The authors provided a comprehensive survey of anomaly detection systems used in the recent years. Intrusion detection was divided into 3 kinds based on technologies used. They are statistical anomaly detection, machine learning based anomaly detection and data mining based anomaly detection. The authors described the various features of anomaly detection technologies in details, represented the algorithms used in the current Anomaly Intrusion Detection Systems, the implements of the algorithms, and also compared the effects of various detection algorithms through the experiment.

Key words: anomaly detection, machine learning, statistical anomaly detection, data mining

中图分类号: 

  • TP393