大数据融合模型的智能化网络安全检测方法  

Intelligent Network Security Detection Method Based on Big Data Fusion Model

作　　者：王鹏[1] 胡宏彬[1] 李勇[1] Wang Peng;Hu Hongbin;Li Yong(Inner Mongolia Electric Power Research Institute,Hohhot 010010,China)

机构地区：[1]内蒙古电力科学研究院,呼和浩特010010

出　　处：《计算机测量与控制》2021年第5期40-44,共5页Computer Measurement &Control

基　　金：内蒙古重大科技项目(NMG2020JG0091)。

摘　　要：针对传统智能化网络安全检测平台处理数据效率低、误差大等问题,文章提出一种新型的解决方案;该方案基于大数据融合模型构建新型的智能化网络安全检测平台,采用卡尔曼滤波算法、采用数据融合分类算法和模糊推理算法3种方法结合构建出数据融合模型来对网络安全检测数据进行运算与处理;其中,采用卡尔曼滤波算法进行改进,对原始网络安全检测数据进行滤波降低噪声干扰,提高数据的精准度;通过SAE稀疏自动编码器自主提取网络安全检测数据的特征信息,之后K-means聚类算法对SAE稀疏自动编码器输出的数据进行处理,通过模糊推理算法调整权值;试验表明,文章所提方案克服了现有技术存在的不足,显著提高了处理数据效率和精准度,在数据量为2 TB的环境下,本研究方法的误差低至6.9%。Aiming at the problems of low data processing efficiency and large error of traditional intelligent network security monitoring platform,this paper proposes a new solution.The scheme builds a new intelligent network security monitoring platform based on the big data fusion model.It uses Kalman filter algorithm,data fusion classification algorithm and fuzzy reasoning algorithm to construct a data fusion model to calculate and process the network security monitoring data.Among them,Kalman filter algorithm is used to improve the original network security monitoring data to reduce noise interference and improve the accuracy of data;the feature information of network security monitoring data is extracted by SAE sparse automatic encoder,and then K-means clustering algorithm is used to process the output data of SAE sparse automatic encoder,and the weight is adjusted by fuzzy inference algorithm.The experimental results show that the proposed scheme overcomes the shortcomings of the existing technology and significantly improves the efficiency and accuracy of data processing.The error of this research method is as low as 6.9%.

关 键 词：网络安全检测 大数据融合 噪声干扰 卡尔曼滤波 模糊推理 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]