Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network
| Autor: | Rongfu Zhou, Jun Lin, Langzhou Liu, Lan Liu, Pengcheng Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Hyperparameter
Article Subject Computer science business.industry Network security Deep learning Node (networking) 020206 networking & telecommunications 02 engineering and technology computer.software_genre Statistical classification Network interface controller Modeling and Simulation 0202 electrical engineering electronic engineering information engineering QA1-939 020201 artificial intelligence & image processing Artificial intelligence Data mining business Game theory computer Host (network) Mathematics |
| Zdroj: | Discrete Dynamics in Nature and Society, Vol 2020 (2020) |
| ISSN: | 1026-0226 |
| DOI: | 10.1155/2020/4163825 |
| Popis: | Based on the design idea of future network, this paper analyzes the network security data sampling and anomaly prediction in future network. Through game theory, it is determined that data sampling is performed on some important nodes in the future network. Deep learning methods are used on the selected nodes to collect data and analyze the characteristics of the network data. Then, through offline and real-time analyses, network security abnormal events are predicted in the future network. With the comparison of various algorithms and the adjustment of hyperparameters, the data characteristics and classification algorithms corresponding to different network security attacks are found. We have carried out experiments on the public dataset, and the experiment proves the effectiveness of the method. It can provide reference for the management strategy of the switch node or the host node by the future network controller. |
| Databáze: | OpenAIRE |
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