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- W4376272242 abstract "Network traffic classification is an important part of network management. For dark net traffic, which is encrypted, The dark net is often used by criminals to transmit illegal information, and the dark net traffic is encrypted. How to detect the traffic of the dark network is of great significance for combating the criminal activities of the dark net. A good network traffic classification algorithm is inseparable from the efficient network traffic attributes. This paper uses a novel network flow-based property, packet interval time entropy, to classify dark web traffic. Because the dark net data transmission needs to forward through multiple routers, and each dark network router needs to resolve the next transmission of the dark network router address, so the dark network packet direct time interval will be different from the ordinary Internet packet time interval, packet interval time entropy can efficiently distinguish the dark web traffic and conventional Internet traffic. Experiments show that the packet interval time entropy property can effectively improve the classification accuracy under the KNN algorithm." @default.
- W4376272242 created "2023-05-13" @default.
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- W4376272242 date "2023-05-12" @default.
- W4376272242 modified "2023-09-26" @default.
- W4376272242 title "Dark net traffic classification based on the K-NN classifier and traffic entropy" @default.
- W4376272242 doi "https://doi.org/10.1117/12.2678890" @default.
- W4376272242 hasPublicationYear "2023" @default.
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