Abstract: At present,in the field of cyber security,due to the shortcomings of long training time and high sensitivity to redundant features,traditional machine learning models have been unable to deal with the increasingly complex network space.To improve the accuracy and efficiency of network security situation awareness for massive and high-dimensional network security elements,a GA-LightGBM network security situation awareness model based on PRF-RFECV feature preference is proposed,which first uses parallel random forest to filter out feature importance,then combines recursive feature elimination with cross-validation to select the optimal feature set,and finally uses the global search property of genetic algorithm to select the optimal parameters of LightGBM model for classification.Experimental simulation shows that the model is more accurate and more efficient than the traditional network security situation awareness algorithm in terms of both accuracy and F1 score.

Key words: Network security situation, Light gradient boosting machine, Random forest, Genetic algorithm