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- W4360603902 abstract "Background network traffic generation is critical to replicating the real network environment in Cyber Range. But how to sufficiently extract the spatio-temporal features of traffic and generate superior background network traffic are still problems for the Cyber Range. In this paper, we propose a background network traffic generative model, DBWE-Corbat. Our solution relies on intelligent feature extraction based on the DB-WE dynamic word embedding method. Which consists of Doc2Vec and two Bidirectional Long Short-Term Memory (Bi-LSTM) layers. Specifically, first we convert the traffic feature tuple data into a static word vector. Then, we capture the spatio-temporal features of the traffic for characterization. Finally, we generate high-quality and numerous background network traffic by learning the feature distribution of small samples based on the contrastive learning model SimCSE. Extensive experiments show that our approach can generate high-quality traffic data. It meets the requirements of cyber range construction compared to other traffic generation methods." @default.
- W4360603902 created "2023-03-24" @default.
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- W4360603902 date "2023-06-01" @default.
- W4360603902 modified "2023-09-28" @default.
- W4360603902 title "DBWE-Corbat: Background network traffic generation using dynamic word embedding and contrastive learning for cyber range" @default.
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- W4360603902 doi "https://doi.org/10.1016/j.cose.2023.103202" @default.
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