安全资源池数据节点异常自动挖掘方法研究

Research on automatic data node anomaly mining method for security resource pool

现有的基于改进Eclat算法的异常数据挖掘方法存在效果精确度低、耗时过多等问题,提出了安全资源池数据节点异常自动挖掘方法。结合特征采集原理计算安全资源池数据节点异常数值,并根据计算结果建立SDN数据模型,通过对采集到的数据进行迭代和聚类处理,简化数据挖掘步骤,实现对异常数据特征的准确挖掘。最后通过实验证实,安全资源池数据节点异常自动挖掘方法具有较高的精确度,并有效解决了数据挖掘耗时过长问题,充分满足研究要求。

The existing anomaly data mining methods based on improved Eclat algorithm have problems such as low effect accuracy and excessive time consumption.A method for automatic mining of data node anomalies in secure resource pools is proposed.Based on the principle of feature collection,the abnormal values of data nodes in the security resource pool are calculated,and an SDN data model is established to iterate and cluster the collected data,thus simplifying data mining steps and realizing accurate mining of abnormal data features.Finally,the experiment proves that the automatic data node anomaly mining method of the security resource pool has high accuracy,and effectively solves the problem that data mining takes too long and fully meets the research requirements.