Details

Autor(en) / Beteiligte

• Li, Zhijian
• Zhao, Dongmei
• Li, Xinghua
• Zhang, Hongbin

Titel

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

Ist Teil von

• EURASIP journal on wireless communications and networking, 2021-09, Vol.2021 (1), p.1-19, Article 177

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.