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• W3128247236 abstract "The emergence of the beyond 5G (B5G) mobile networks has provided us with a variety of services and enriched our lives. The B5G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information security, including network trust, security, and privacy. However, security issues have also been raised, which greatly threaten our information security and privacy. For example, malwares use domain generation algorithms (DGAs) to generate huge quantities of domain names and then induce users to access to steal private information, which greatly threatens our information security. In this paper, we propose an approach to detect the malicious domain name by extracting and analyzing the features using deep neural network. Unlike traditional algorithms that are generally built on tedious feature engineering, our paper utilizes the hierarchy of bidirectional recurrent neural networks (HBiRNN) to extract effective semantic features instead of traditional methods. We use the discriminator based on HBiRNN (D-HBiRNN) to detect malicious websites. This experiment verifies the validity of the algorithm and compares it with the traditional algorithm based on feature engineering. Moreover, the superiority of the algorithm is proved." @default.
• W3128247236 created "2021-02-15" @default.
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• W3128247236 date "2021-10-01" @default.
• W3128247236 modified "2023-10-16" @default.
• W3128247236 title "Hierarchical Bidirectional RNN for Safety-Enhanced B5G Heterogeneous Networks" @default.
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• W3128247236 doi "https://doi.org/10.1109/tnse.2021.3055762" @default.
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