Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

Abstract

The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarmoptimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore, a prediction model of TCAN-BiGRU is established respectively for each subsequence. TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism (CAM) to extract important feature information from TCN. BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction. Besides, IQPSO is proposed to optimize the hyperparameters of BiGRU. Finally, the prediction results of the subsequence are superimposed to obtain the final predicted value. On the one hand, IQPSO compares with other optimization algorithms in the experiment, whose performance can find the optimum value of the benchmark function many times, showing that IQPSO performs better. On the other hand, the established prediction model compares with the traditional prediction methods through the simulation experiment, whose coefficient of determination is up to 0.999 on both sets, indicating that the combined prediction model established has higher prediction accuracy.