基于数据挖掘的入侵检测

Intrusion Detection Based on Data Mining

随着计算机网络在现代社会中扮演日益重要的角色 ,信息安全成为信息技术研究领域最重要的研究课题之一。而入侵构成了严重的安全风险 ,如何有效防范和检测入侵行为是信息监管中的热点研究问题。传统入侵检测模型的建立过程效率低 ,研究成本高 ,而数据挖掘在未知知识获取方面具有独特优势 ,因此基于数据挖掘的入侵检测成为研究热点。针对入侵现状、入侵检测和数据挖掘研究及开发状况 ,笔者分析了基于数据挖掘的入侵检测研究背景、体系结构、研究方法、所需解决的问题及今后的研究方向。

As computer networks play increasingly vital roles in modern society, information security becomes one of the most important research issues in the field of information technology. But intrusions cause a serious security risk, how to efficiently prevent and detect intrusions becomes one of hot research problems in the field of information supervision. The traditional process of building the model of intrusion detection is slow, whose cost of research and development is high. However, data mining has unique advantages in acquiring unknown knowledge. So, intrusion detection based on data mining becomes a hot issue. The research background, architectures, techniques, problems to be solved and the future direction are discussed after analyzing current status of network intrusion and situation of R&D on intrusion detection and data mining.