SSA and BPNN based Efficient Situation Prediction Model for Cyber Security

Establishing an effective situation prediction model for cyber security can know the active situation of future network malicious events in advance, which plays a vital role in cyber security protection. However, traditional models cannot achieve sufficient prediction accuracy when predicting cyber situations. To solve this problem, the initial location information of the sparrow population is optimized, and a sparrow search algorithm based on the Tent map is proposed. Then, the BP neural network is optimized using the improved sparrow search algorithm. Finally, a situation prediction model based on the sparrow search algorithm and BP neural network is proposed, namely T-SSA-BPNN. The simulation results show that the convergence speed and global search ability of the prediction model are improved. It can effectively predict the network security situation with high accuracy.