基于可视化技术的海量数据安全特征提取算法

Massive data security feature extraction algorithm based on visualization technology

由于传统算法在海量数据安全特征提取中应用效果不佳,不仅提取误差比较大,而且提取时间比较长,无法达到预期的特征提取效果,提出基于可视化技术的海量数据安全特征提取算法。在网络日志记录集中拾取与数据提取源相关的海量数据,利用聚类分析法对海量数据进行聚类分析,以数据的可靠性对海量数据安全特征进行识别,利用可视化技术对安全特征进行统计提取,以此完成基于可视化技术的海量数据安全特征提取。实验证明,设计方法数据安全特征提取误差小于1%,提取时间在1s以内,可以有效保证海量数据安全特征提取精度和速度。

Due to the poor application effect of traditional algorithms in the extraction of massive data security features,not only the extraction error is relatively large,but also the extraction time is relatively long,which cannot achieve the expected feature extraction effect,and a massive data security feature extraction algorithm based on visualization technology is proposed.In the network log record set,the massive data related to the data extraction source is picked up,the cluster analysis method is used to cluster and analyze the massive data,the security features of massive data are identified by the reliability of the data,and the security features are statistically extracted by the visualization technology,so as to complete the extraction of massive data security features based on visualization technology.Experiments have shown that the data security feature extraction error of the design method is less than 1%,and the extraction time is less than 1s,which can effectively ensure the accuracy and speed of extracting the security features of massive data.