research-article

A Novel QUIC Traffic Classifier Based on Convolutional Neural Networks

Authors:

Abdelhamid MelloukAuthors Info & Claims

2018 IEEE Global Communications Conference (GLOBECOM)

Pages 1 - 6

https://doi.org/10.1109/GLOCOM.2018.8647128

Published: 09 December 2018 Publication History

Abstract

Nowadays, network traffic classification plays an important role in many fields including network management, intrusion detection system, malware detection system, etc. Most of the previous research works concentrate on features extracted in the non-encrypted network traffic. However, these features are not compatible with all kind of traffic characterization. Google's QUIC protocol (Quick UDP Internet Connection protocol) is implemented in many services of Google. Nevertheless, the emergence of this protocol imposes many obstacles for traffic classification due to the reduction of visibility for operators into network traffic, so the port and payload- based traditional methods cannot be applied to identify the QUIC- based services. To address this issue, we proposed a novel technique for traffic classification based on the convolutional neural network which combines the feature extraction and classification phase into one system. The proposed method uses the flow and packet-based features to improve the performance. In comparison with current methods, the proposed method can detect some kind of QUIC-based services such as Google Hangout Chat, Google Hangout Voice Call, YouTube, File transfer and Google play music. Besides, the proposed method can achieve the microaveraging F1-score of 99.24 percent.

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Cited By

Cebere BRossow C(2024)Understanding Web Fingerprinting with a Protocol-Centric ApproachProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678910(17-34)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678910
Joarder YFung C(2024)QUICPro: Integrating Deep Reinforcement Learning to Defend against QUIC Handshake Flooding AttacksProceedings of the 2024 Applied Networking Research Workshop10.1145/3673422.3674901(94-96)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3673422.3674901
Hoang NDuong CVu MNguyen HTran XTong VTran H(2023)A New Transfer Learning-Based Traffic Classification Algorithm for a Multi-Domain SDN NetworkProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628804(235-242)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3628804
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2018 IEEE Global Communications Conference (GLOBECOM)

Dec 2018

6265 pages

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IEEE Press

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Published: 09 December 2018

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Cited By

Cebere BRossow C(2024)Understanding Web Fingerprinting with a Protocol-Centric ApproachProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678910(17-34)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678910
Joarder YFung C(2024)QUICPro: Integrating Deep Reinforcement Learning to Defend against QUIC Handshake Flooding AttacksProceedings of the 2024 Applied Networking Research Workshop10.1145/3673422.3674901(94-96)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3673422.3674901
Hoang NDuong CVu MNguyen HTran XTong VTran H(2023)A New Transfer Learning-Based Traffic Classification Algorithm for a Multi-Domain SDN NetworkProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628804(235-242)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3628804
Bozkır RCi̇ci̇oğlu MÇalhan AToğay C(2023)A new platform for machine-learning-based network traffic classificationComputer Communications10.1016/j.comcom.2023.05.010208:C(1-14)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1016/j.comcom.2023.05.010
Zhao LCai LYu AXu ZMeng DHung CCerny TShin DBechini A(2020)A novel network traffic classification approach via discriminative feature learningProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373844(1026-1033)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3373844

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