Network Security Situation Estimation based on Big Data

Xuan Huang

1School of Software and Internet of things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

2Management science and engineering, Faculty of Management, Nanchang University, Nanchang, 330031, China

Abstract: The traditional method uses the terminal network monitoring method to carry out network security estimation. due to the strong power attenuation of the terminal of the network communication channel, the accuracy of the security situation estimation is not high and the performance detection effect is poor. Therefore, a network security estimation and situation prediction algorithm based on adaptive data classification and virus infection membership feature extraction is proposed in this paper. The network security estimation model in large data environment is constructed. The adaptive data classification algorithm is used to cluster and evaluate network attack information data, extract the infection membership feature of network attack virus data, and realize network security situation prediction and virus attack. Simulation results show that the algorithm has higher prediction accuracy for virus data flow, can effectively realize network virus flow prediction and data detection in different scenarios, and improves the network's ability to resist virus attacks in large data environments.

Keywords: Big data; Network security; Antiviral ability