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- W4386999805 abstract "<div align=center><span lang=EN-US>Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a <a name=_Hlk143263730></a>DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification.</span></div>" @default.
- W4386999805 created "2023-09-25" @default.
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- W4386999805 date "2023-12-01" @default.
- W4386999805 modified "2023-09-30" @default.
- W4386999805 title "A novel deep-learning based approach to DNS over HTTPS network traffic detection" @default.
- W4386999805 doi "https://doi.org/10.11591/ijece.v13i6.pp6691-6700" @default.
- W4386999805 hasPublicationYear "2023" @default.
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