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Encrypted traffic classification of decentralized applications on ethereum using feature fusion

Authors:

Yiting LiuAuthors Info & Claims

IWQoS '19: Proceedings of the International Symposium on Quality of Service

Article No.: 18, Pages 1 - 10

https://doi.org/10.1145/3326285.3329053

Published: 24 June 2019 Publication History

Abstract

With the prevalence of blockchain, more and more Decentralized Applications (DApps) are deployed on Ethereum to achieve the goal of communicating without supervision. Users habits may be leaked while these applications adopt SSL/TLS to encrypt their transmission data. Encrypted protocol and the same blockchain platform bring challenges to the traffic classification of DApps. Existing encrypted traffic classification methods suffer from low accuracy in the situation of DApps.

In this paper, we design an efficient method to fuse features of different dimensions for DApp fingerprinting. We firstly analyze the reason why existing methods do not perform well before proposing to merge features of different dimensions. Then we fuse these features by a kernel function and propose a fusion feature selection method to select appropriate features to fuse. Applying features that have been fused to the machine learning algorithm can construct a strong classifier. The experiment results show that the accuracy of our method can reach more than 90%, which performs better than state-of-the-art classification approaches.

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Cited By

Peng WCui LCai WWang WCui XHao ZYun X(2025)Bottom Aggregating, Top Separating: An Aggregator and Separator Network for Encrypted Traffic UnderstandingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.352931620(1794-1806)Online publication date: 2025
https://doi.org/10.1109/TIFS.2025.3529316
Rao BZhu GDing QChen DCao MCao YWang F(2025)A steganographic backdoor attack scheme on encrypted trafficPeer-to-Peer Networking and Applications10.1007/s12083-024-01893-718:2Online publication date: 17-Jan-2025
https://doi.org/10.1007/s12083-024-01893-7
Liu YJia JWu JAi JShen MZhu L(2025)Encrypted Malware Traffic Detection Via Time-Frequency Domain AnalysisAlgorithms and Architectures for Parallel Processing10.1007/978-981-96-1548-3_7(98-110)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-981-96-1548-3_7
Show More Cited By

Encrypted traffic classification of decentralized applications on ethereum using feature fusion
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image ACM Other conferences

IWQoS '19: Proceedings of the International Symposium on Quality of Service

June 2019

420 pages

ISBN:9781450367783

DOI:10.1145/3326285

Copyright © 2019 ACM.

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Publication History

Published: 24 June 2019

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IWQoS '19

IWQoS '19: IEEE/ACM International Symposium on Quality of Service

June 24 - 25, 2019

Arizona, Phoenix

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Cited By

Peng WCui LCai WWang WCui XHao ZYun X(2025)Bottom Aggregating, Top Separating: An Aggregator and Separator Network for Encrypted Traffic UnderstandingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.352931620(1794-1806)Online publication date: 2025
https://doi.org/10.1109/TIFS.2025.3529316
Rao BZhu GDing QChen DCao MCao YWang F(2025)A steganographic backdoor attack scheme on encrypted trafficPeer-to-Peer Networking and Applications10.1007/s12083-024-01893-718:2Online publication date: 17-Jan-2025
https://doi.org/10.1007/s12083-024-01893-7
Liu YJia JWu JAi JShen MZhu L(2025)Encrypted Malware Traffic Detection Via Time-Frequency Domain AnalysisAlgorithms and Architectures for Parallel Processing10.1007/978-981-96-1548-3_7(98-110)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-981-96-1548-3_7
Zhou XXiao XLi QZhang BHu GLuo XZhang TQuek TGao DZhou JCardenas A(2024)CapsuleFormer: A Capsule and Transformer combined model for Decentralized Application encrypted traffic classificationProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637664(1418-1429)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3637664
Zhu YTao JWang HYu LLuo YQi TWang ZXu Y(2024)DGNN: Accurate Darknet Application Classification Adopting Attention Graph Neural NetworkIEEE Transactions on Network and Service Management10.1109/TNSM.2023.334458021:2(1660-1671)Online publication date: Apr-2024
https://doi.org/10.1109/TNSM.2023.3344580
Seo MKim JYou MShin SKim J(2024)gShock: A GNN-Based Fingerprinting System for Permissioned Blockchain Networks Over Encrypted ChannelsIEEE Access10.1109/ACCESS.2024.346958312(146328-146342)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3469583
Liu LYu YWu YHui ZLin JHu J(2024)Method for multi-task learning fusion network traffic classification to address small sample labelsScientific Reports10.1038/s41598-024-51933-814:1Online publication date: 30-Jan-2024
https://doi.org/10.1038/s41598-024-51933-8
Shen MTang XWang WZhu LShen MTang XWang WZhu L(2024)User Behavior Identification via Traffic Analysis in Web 3.0Security and Privacy in Web 3.010.1007/978-981-97-5752-7_6(95-116)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-981-97-5752-7_6
Yang CGuo LGu ZGou GLin XXiong GGuan Y(2024)Anti-Packet-Loss Encrypted Traffic Classification via Masked AutoencoderWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71464-1_7(79-92)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-71464-1_7
Steffi PSam Emmanuel WRani P(2024)A Cost-Sensitive Sparse Auto-encoder Based Feature Extraction for Network Traffic Classification Using CNNProceedings of 4th International Conference on Artificial Intelligence and Smart Energy10.1007/978-3-031-61471-2_17(231-244)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-3-031-61471-2_17
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