Detection Method of Stealing Complex Network Attack Considering Security Situation Awareness

Abstract

Under the premise that big data, smart cities, mobile internet, cloud computing, and the Internet of Things have become the market for information ownership, cyber security threats are becoming more and more serious. Network attacks have begun to shift to the direction of stealing complex network attacks, and it is of great significance to study the prediction methods of stealing complex network attacks. The purpose of this article is to consider the security situational awareness of the theft-type complex network attack monitoring method for research. This paper introduces and divides the stages of the stealing complex network attack in detail, proposes an alarm algorithm based on the aggregation stage of redundant relationships and an algorithm based on the causal probability correlation attack stage, and illustrates the process of extracting attack scenarios. This article proposes and compares three network security situational awareness models and introduces in detail the two model structures of random forest and star. Experimental data shows that before optimization, the detection rates of Naive Bayes, Random Forest, and Neural Network are 75%, 80%, and 65%. After optimization, the detection rates of Naive Bayes, Random Forest and Neural Network are 96%, 90%, and 92%. This is because the original data has been effectively preprocessed after the first stage of situational awareness, so that the neural network model and the naive Bayesian structure model overcome the sensitivity to the shortcomings of the data set, resulting in a higher detection rate.