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

EURASIP Journal on Wireless Communications and Networking

Zhijian Li¹  Dongmei Zhao¹  Xinghua Li²  Hongbin Zhang³ 

[1] School of Computer and Cyber Security, Hebei Normal University;School of Cyber Engineering, Xidian University;School of Information Science and Engineering, Hebei University of Science and Technology;

关键词: Situation prediction;    Smart cities;    Feature separation;    Global attention;    Limited attention;   

DOI  :  10.1186/s13638-021-02050-x

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown