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Realization of intrusion detection system based on the improved data mining technology

机译：基于改进数据挖掘技术的入侵检测系统的实现

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摘要

On the basis of further analyzing the operational mechanism of the existing intrusion detection system model, in allusion to the existing problem the powerless, high false negative rate, low detection efficiency and the lack of the rule base automatic extension mechanism to unknown aggressive behavior for existing detection mechanisms, Combining the relevant knowledge of data mining technology, then to design one improved network intrusion detection system model based on data mining, combined misuse detection and anomaly detection. In the model, we select the K-means algorithm in clustering analysis and the Apriori algorithm in association rule mining and improve it. Applying the improved K-means algorithm to achieve normal behavior classes and data separation module, then utilizing the improved Apriori algorithm to achieve automatic extension of the rule base. Finally, by the experiment to verify the function of the two algorithms.
机译：在进一步分析现有入侵检测系统模型的运行机制的基础上，针对现有问题，无能为力，假阴性率高，检测效率低以及缺乏针对未知攻击行为的规则库自动扩展机制检测机制，结合数据挖掘技术的相关知识，然后设计一种基于数据挖掘，组合滥用检测和异常检测的改进的网络入侵检测系统模型。在模型中，我们选择聚类分析中的K-means算法和关联规则挖掘中的Apriori算法并对其进行改进。应用改进的K-means算法实现正常行为分类和数据分离模块，然后利用改进的Apriori算法实现规则库的自动扩展。最后，通过实验验证了两种算法的功能。

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