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Performance Analysis of Classification Techniques by using Multi Agent Based Intrusion Detection System

机译:基于多代理的入侵检测系统的分类技术性能分析

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In this paper we have designed Agent based intrusion detection system (ABIDS) where agents will travel between connected client systems from server in a client-server network. The agent will collect information from client systems through data collecting agents. It will then categorize and associate data in the form of report, and send the same to server. Intrusion detection system (IDS) will support runtime addition of new ability to agents. We have illustrated the design of ABIDS and show the performance of ABIDS with various classification techniques that could produce good results. The motive of the work is to examine the best performance of ABIDS among various classification techniques for huge data. Moreover sophisticated NSL KDD dataset are used during experiments for more sensible assessment than the novel KDD 99 dataset.
机译:在本文中,我们设计了基于代理的入侵检测系统(ABIDS),其中代理将在客户端-服务器网络中的服务器之间在连接的客户端系统之间传播。该代理将通过数据收集代理从客户端系统收集信息。然后它将以报告的形式对数据进行分类和关联,并将其发送到服务器。入侵检测系统(IDS)将支持在运行时向代理添加新功能。我们已经说明了ABIDS的设计,并通过各种分类技术展示了ABIDS的性能,这些分类技术可以产生良好的效果。这项工作的目的是检验ABIDS在针对大数据的各种分类技术中的最佳性能。而且,在实验过程中使用了复杂的NSL KDD数据集,比新颖的KDD 99数据集更明智的评估。

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