Network Security Situation Prediction Based on MR-SVM

Abstract

The support vector machine (SVM) is verified to be effective for predicting cyber security situations, however, the long training time of the prediction model is a drawback to its use. To address this, a cyber security situation prediction model based on MapReduce and the SVM is proposed. The base classifier for this model uses an SVM, and parameter optimization is performed by the Cuckoo Search (CS) to determine the optimal parameters of the SVM. Considering the problem of time cost when a data set is large, we choose to use MapReduce to perform distributed training on SVMs to improve training speed. 'Map' is used to map distributed training network security situation data, and 'Reduce' merges and sorts the prediction results. Experimental results show that the proposed prediction model has improved the accuracy and decreased the training time cost compared to the traditional model.