The Application of Data Mining Technology in Anomaly Detection

ZHAO Yu-ming; TENG Shao-hua; ZHANG Wei; WU Nai-qi

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The Application of Data Mining Technology in Anomaly Detection

更新时间：2026-05-27

- The Application of Data Mining Technology in Anomaly Detection
- Vol. 22, Issue 3, Pages: 48-52(2005)
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- Online First：02 July 2005，
  
  Published：2005
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赵玉明, 滕少华, 张巍, 伍乃骐. 异常入侵检测中数据挖掘技术RIPPER的应用[J]. 广东工业大学学报, 2005, 22(3): 48-52.

ZHAO Yu-ming, TENG Shao-hua, ZHANG Wei, et al. The Application of Data Mining Technology in Anomaly Detection[J]. 2005, 22(3): 48-52.
赵玉明, 滕少华, 张巍, 伍乃骐. 异常入侵检测中数据挖掘技术RIPPER的应用[J]. 广东工业大学学报, 2005, 22(3): 48-52. DOI：

ZHAO Yu-ming, TENG Shao-hua, ZHANG Wei, et al. The Application of Data Mining Technology in Anomaly Detection[J]. 2005, 22(3): 48-52. DOI：

摘要

介绍了入侵检测技术的分类以及数据挖掘技术在入侵检测中的应用

并阐述了构建的基于数据挖掘算法RIPPER的异常入侵检测系统的设计与实现.

Abstract

This paper introduces the categories of intrusion detection and the application of data mining technology in anomaly detection.It also describes the design and the implementation of the anomaly IDS based on data mining algorithms

RIPPER.

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Keywords

references

Wenke Lee,Salvatore J Stolfo.Learning patterns from unix process execution traces for intrusion detection. In AAAI Workshop on AI Approaches to Fraud Detection and Risk Management[C], AAAI Press . 1997

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