Dynamic Network Security Situation Prediction based on Bayesian Attack Graph and Big Data

Abstract

In order to predict attack behavior efficiently and dynamically and to quantify the network security situation, a dynamic network security situation prediction method based on big data and Bayesian attack graph is proposed. First, using big data technology to fuse network security situation factors, then use vulnerability prediction algorithm to predict the future number of vulnerabilities in real time and finally combine the new vulnerability with Bayesian attack graph to infer the attacker’s subsequent attack behavior. According to the predicted results, the network security situation is quantified. Experiments show that the method can predict the attack behavior and efficiently quantify the network security situation accurately.