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Akademický článek

The Attention-Based Autoencoder for Network Traffic Classification with Interpretable Feature Representation

Autor: Jun Cui, Longkun Bai, Xiaofeng Zhang, Zhigui Lin, Qi Liu

Publikováno v: Symmetry, Vol 16, Iss 5, p 589 (2024)

Network traffic classification is crucial for identifying network applications and defending against network threats. Traditional traffic classification approaches struggle to extract structural features and suffer from poor interpretability of featu

Externí odkaz: https://doaj.org/article/bb184fe8f3f541c79534dea055f5a9cd

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Akademický článek

Semi-2DCAE: a semi-supervision 2D-CNN AutoEncoder model for feature representation and classification of encrypted traffic

Autor: Jun Cui, Longkun Bai, Guangxu Li, Zhigui Lin, Penggao Zeng

Publikováno v: PeerJ Computer Science, Vol 9, p e1635 (2023)

Traffic classification is essential in network-related areas such as network management, monitoring, and security. As the proportion of encrypted internet traffic rises, the accuracy of port-based and DPI-based traffic classification methods has decl

Externí odkaz: https://doaj.org/article/16f256760da84abd8a6d7a90d12209be

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Akademický článek

Semi-2DCAE: a semi-supervision 2DCNN AutoEncoder model for feature representation and classification of encrypted traffic.

Autor: Jun Cui, Longkun Bai, Guangxu Li, Zhigui Lin, Penggao Zeng

Publikováno v: PeerJ Computer Science; Nov2023, p1-30, 30p

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