Neural network security situation prediction method based on attention-GRU

Ye Yuan; Wenli Xu

doi:10.1117/12.2646520

23 August 2022 Neural network security situation prediction method based on attention-GRU

Ye Yuan, Wenli Xu

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123300E (2022) https://doi.org/10.1117/12.2646520
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

In terms of network security visualization, due to the diversity and complexity of security data, visualization technology can be used to analyze the security of the entire information system, detect intrusions, investigate network traffic, and verify the rules and policies of security sensors. The traditional prediction method is difficult to accurately model the relationship between the situation value and the time change. The prediction error is large. This paper proposes a cycle gating unit coding prediction method based on the attention mechanism. The GRU network is used as the basic neural network processing unit. The attention mechanism is introduced into the traditional GRU network layer to calculate the attention weight of different safety data. After data fusion, the GRU network is input to predict the situation value. Simulation results show that the proposed method has smaller prediction error, faster convergence speed, and lower complexity. The architecture proposed in this paper has a good dimensionality reduction effect and classification accuracy of situation elements, achieves excellent cyberspace situational awareness, and improves network security through the visual operation of cyberspace.

Citation Download Citation

Ye Yuan and Wenli Xu "Neural network security situation prediction method based on attention-GRU", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123300E (23 August 2022); https://doi.org/10.1117/12.2646520

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