Research on Quantitative Analysis of Security of Network Risk Based on Big Data

Abstract

In big data environment, due to the rapid clustering and data forwarding of data in the multi-path transmission link layer, it is vulnerable to network virus implantation and intrusion. By quantifying the network risk security situation in big data environment, improve the ability to withstand risks. A quantitative analysis and prediction algorithm of network risk security situation in big data environment based on big data fuzzy C-means clustering and network intrusion information spectrum feature extraction is proposed. The quantitative analysis model of network risk security situation under the environment of big data is constructed. Big data fuzzy C-means clustering algorithm is used to cluster and evaluate the statistical characteristic information data of network intrusion. The high-order spectrum characteristics of big data are analyzed quantitatively by extracting the security situation of network risk, and the quantitative assessment of network risk security situation and the detection of network intrusion are realized. The simulation results show that the algorithm has high accuracy in evaluating the situation of network risk security, and realizes the quantitative assessment and intrusion detection of network risk security situation in different scenarios, and improves the ability of the network to resist network intrusion under the environment of big data.