Abstract
Big data processing technology has attracted a lot of attention due to its forecasting and warning of Internet security situation. The current risk assessment system still has problems such as high false alarm rate and excessive reliance on expert knowledge in the security defense system. Based on the big data-driven principle, this paper constructs a hierarchical local area network security risk event prediction model and proposes a predictive complex event processing method. The model building process is evolved and improved on the basis of the scoring function. The establishment method of vulnerability database and vulnerability association database is introduced in detail. At the same time, the problem of the difference between the structure and identification method of the information in the information database and the vulnerability database is solved, and the effect of timely modification when the data do not match is realized. Experimental results show that the algorithm has an accuracy of 98.75% and a fault tolerance rate of 0.0035, which promotes the accuracy of the network risk assessment results based on multistage network attacks.
Highlights
In the era of big data, the Internet, sensor networks, social networks, etc. continue to generate a large amount of data
Aiming at the problem that the hierarchical local evolution model may not be able to predict in a short time under the data-driven situation, this paper proposes a predictive complex event processing method based on a variable structure dynamic hierarchical local area network
Historical data are divided by offline context clustering, and different clusters are obtained. e data divided into each cluster use a scoring search method to compare the corresponding data. e hierarchical local area network is used for learning, and the Gaussian mixture model is used for approximate inference
Summary
Big data processing technology has attracted a lot of attention due to its forecasting and warning of Internet security situation. e current risk assessment system still has problems such as high false alarm rate and excessive reliance on expert knowledge in the security defense system. E current risk assessment system still has problems such as high false alarm rate and excessive reliance on expert knowledge in the security defense system. Based on the big data-driven principle, this paper constructs a hierarchical local area network security risk event prediction model and proposes a predictive complex event processing method. The problem of the difference between the structure and identification method of the information in the information database and the vulnerability database is solved, and the effect of timely modification when the data do not match is realized. Experimental results show that the algorithm has an accuracy of 98.75% and a fault tolerance rate of 0.0035, which promotes the accuracy of the network risk assessment results based on multistage network attacks
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