Hybrid neural networks for network security situation prediction: the CNN-LSTM-MulAttNet model

This paper proposed a hybrid Network Security Situation Prediction (NSSP) sequence neural network model, which integrates CNN, LSTM, and Multihead Attention mechanisms, and experimented with various techniques to enhance the nonlinearity and generalization ability of neural networks. We constructed NSSP sequence datasets on two publicly available anomaly detection datasets and validated the effectiveness and accuracy of our proposed method. The RMSE values of our proposed method on the testing datasets of UNSW-NB15-January 22, UNSW-NB15-February 17, and KDDCup99 are 2.262E-07, 2.275E-03, and 4.938E-05 respectively. The MAE values are 4.756E-04, 2.667E-02, and 3.322E-04 respectively.