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WFF-EGNN: Encrypted Traffic Classification Based on Weaved Flow Fragment via Ensemble Graph Neural Networks by Zihan Chen; Guang Cheng; Dandan Niu; Xing Qiu; Yuyu Zhao; Yuyang Zhou is a Computer Science article available to read on EtoBox.

What is WFF-EGNN: Encrypted Traffic Classification Based on Weaved Flow Fragment via Ensemble Graph Neural Networks about?

Traffic analysis plays an essential role in network management and security protection under the premise of fully protecting user privacy. Unfortunately, encryption dramatically reduces the disclosure of traffic information, making encrypted traffic analysis more challenging than plaintext traffic analysis, especially in the environment of new encryption protocols (e.g., TLS-1.3, QUIC). The existing tensor-based methods mainly focus on optimizing packet length sequence features and introducing the latest deep learning model. However, the tensor-based features cannot sufficiently express the structured non-Euclidean Markov properties inside the encrypted traffic. This paper proposes a novel traffic graphical expression model named Weaved Flow Fragment (WFF) to transform a packet sequence into a graph, which better represents the packet sequence's inner relationship than the tensor. WFF also considers the co-evolution relationship and the cross-direction change relationship in the bidirectional flow, breaking through the limitation that the tensor-like length sequence only considers the adjacent Markov properties. Then, we use the latest graph convolutional networks, gated graph neur

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Author

Zihan Chen; Guang Cheng; Dandan Niu; Xing Qiu; Yuyu Zhao; Yuyang Zhou

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)