Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200126-7.doi: 10.11896/jsjkx.241200126

• Information Security • Previous Articles     Next Articles

Classification of Encrypted Application Traffic Enhanced by Multi-level GraphRepresentation

WANG Zhihong1, LIU Shengran2, CHI Zegui3, YANG Ying1   

  1. 1 R&D Center of Network Investigation Technology,The Third Research Institute of The Ministry of Public Security,Shanghai 200000,China
    2 NET Police Corps,Guangdong Provincial Public Security,Guangzhou 510000,China
    3 NET Police Troops,Huizhou Municipal Public Security Bureau,Huizhou,Guangdong 516000,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:WANG Zhihong,born in 1990,Ph.D,assistant professor,is a member of CCF(No.49479M).His main research interests include nature language process,network and information security.
  • Supported by:
    Ministry of Public Security Science and Technology Strengthening Police Project(2023JC21)and National Key R&D Program of China(2021YFB101405)．

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Abstract: With the increasing demand for privacy protection and data security,more and more applications and services use traffic encryption technology.While protecting users’ privacy,it also provides convenience for illegal users,seriously threatening network security defense and supervision.Due to the limitation of single and multiple records in representation,this paper proposes a model of encrypted application traffic enhanced by the multi-level graph representation.The proposed method constructs packet graphs based on multi-type interactive information in a single record,such as payload length,direction,sequence,and cluster information.Furtherly,multi-record graphs are constructed based on flow sequence association to break through the limitation of a single record.Finally,the graph neural network is introduced to realize the representation of traffic based on packet graphs and record graphs.Experiments are carried out on the ISCX VPN-nonVPN 2016 dataset,which is a widely used open-source dataset in the encrypted traffic classification area.Experimental results show the overall classification accuracy of the proposed method on VPN and non-VPN reach 98.1% and 89.2% respectively,and the F1 score is significantly improved compared with Text-based-CNN,k-GNN etc.

Key words: Encrypted traffic, Encrypted application classification, Graph neural network, Package graph, Record graph

CLC Number: 

  • TP391
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