An efficient framework for intrusion detection based on data mining

Weidong Li, Kejun Zhang, Boqun Li, Bingru Yang
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引用次数: 4

Abstract

A multi-layer intrusion detection framework is proposed in this paper. Comparing to the traditional system, the framework has sources from all the respects of host computer and network, and calculates connecting volume for each active connection, thus only the suspicious connections would be analyzed, more than 80% packets are normal, and don't need processing, influence to the system speed is very little. All the information of the host computer is combined to a union, and the properties are expanded and enhanced for the data mining engine, so the mining process is efficient and accurate. Fuzzy mining can also be used in intrusion detecting and rule sets comparing. The framework provides abilities of detection, report and response. Experimental results show the rapidness and accuracy of the proposed framework
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一种高效的基于数据挖掘的入侵检测框架
提出了一种多层入侵检测框架。与传统系统相比,该框架从主机和网络的各个方面都有来源,并对每个活动连接计算连接量,因此只分析可疑连接,80%以上的数据包是正常的,不需要处理,对系统速度的影响很小。将主机的所有信息组合成一个union,并对数据挖掘引擎的属性进行扩展和增强,从而提高挖掘过程的效率和准确性。模糊挖掘还可用于入侵检测和规则集比较。该框架提供了检测、报告和响应的能力。实验结果表明了该框架的快速性和准确性
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