Abstract
Malware steals private information by randomly generating a large number of malicious domain names every day using domain generation algorithms (DGAs), which pose a great threat to our daily Internet activity. To improve recognition accuracy for these malicious domain names, this paper proposes a malicious domain name detection algorithm based on deep neural networks to capture the characteristics of malicious domain names. The resulting model is called a Discriminator based on Hierarchical Bidirectional Recurrent Neural Networks (D-HBiRNN).
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Yan, X., Cui, B., Li, J. (2018). Malicious Domain Name Recognition Based on Deep Neural Networks. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_43
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DOI: https://doi.org/10.1007/978-3-030-05345-1_43
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