Network security situation prediction based on feature separation and dual attention mechanism

Abstract

With the development of smart cities, network security has become more and more important. In order to improve the safety of smart cities, a situation prediction method based on feature separation and dual attention mechanism is presented in this paper. Firstly, according to the fact that the intrusion activity is a time series event, recurrent neural network (RNN) or RNN variant is used to stack the model. Then, we propose a feature separation method, which can alleviate the overfitting problem and reduce cost of model training by keeping the dimension unchanged. Finally, limited attention is proposed according to global attention. We sum the outputs of the two attention modules to form a dual attention mechanism, which can improve feature representation. Experiments have proved that compared with other existing prediction algorithms, the method has higher accuracy in network security situation prediction. In other words, the technology can help smart cities predict network attacks more accurately.

Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks

“…Li et al proposed a model based on Recursive Neural Networks (RNN) and its variants, which effectively improved the accuracy of feature representation through recursive stacking network structures and feature separation methods based on the temporal characteristics of intrusion data [16]. However, this approach still faces the problem of overfitting.…”