research-article

Internet Traffic Classification Using an Ensemble of Deep Convolutional Neural Networks

Authors:

Mahmoud Abbasi,

Amir Taherkordi,

Mohammed KaosarAuthors Info & Claims

FlexNets '21: Proceedings of the 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility

Pages 38 - 43

https://doi.org/10.1145/3472735.3473386

Published: 23 August 2021 Publication History

Abstract

Network traffic classification (NTC) has attracted considerable attention in recent years. The importance of traffic classification stems from the fact that data traffic in modern networks is extremely complex and ever-evolving in different aspects, e.g. volume, velocity and variety. The inherent security requirements of Internet-based applications also highlights further the role of traffic classification. Gaining clear insights into the network traffic for performance evaluation and network planning purposes, network behavior analysis, and network management is not a trivial task. Fortunately, NTC is a promising technique to gain valuable insights into the behavior of the network, and consequently improve the network operations. In this paper, we provide a method based on deep ensemble learning to classify the network traffic in communication systems and networks. More specifically, the proposed method combines a set of Convolutional Neural Network (CNN) models into an ensemble of classifiers. The outputs of the models are then combined to generate the final prediction. The results of performance evaluation show that the proposed method provides an average accuracy rate of 98% for the classification of traffic (e.g., FTP-DATA, MAIL, etc.) in the Cambridge Internet traffic dataset.

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Cited By

Merlin CPaulraj GJebadurai ISharon E(2024)Hyper Parameter Optimization for Ensemble Techniques in Classifying QUIC Traffic2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS)10.1109/ICC-ROBINS60238.2024.10534007(621-626)Online publication date: 17-Apr-2024
https://doi.org/10.1109/ICC-ROBINS60238.2024.10534007
Gioacchini LSantos WLopes BDrago IMellia MAlmeida JGonçalves M(2024)Explainable Stacking Models based on Complementary Traffic Embeddings2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00035(261-272)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00035
Khani PMoeinaddini EAbnavi NShahraki A(2024)Explainable artificial intelligence for feature selection in network traffic classificationTransactions on Emerging Telecommunications Technologies10.1002/ett.497035:4Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1002/ett.4970
Show More Cited By

Index Terms

Internet Traffic Classification Using an Ensemble of Deep Convolutional Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Networks
  1. Network performance evaluation
    1. Network performance analysis
  2. Network properties
    1. Network security
      1. Security protocols

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cover image ACM Conferences

FlexNets '21: Proceedings of the 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility

August 2021

66 pages

ISBN:9781450386340

DOI:10.1145/3472735

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication

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New York, NY, United States

Publication History

Published: 23 August 2021

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SIGCOMM '21: ACM SIGCOMM 2021 Conference

August 23, 2021

Virtual Event, USA

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Cited By

Merlin CPaulraj GJebadurai ISharon E(2024)Hyper Parameter Optimization for Ensemble Techniques in Classifying QUIC Traffic2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS)10.1109/ICC-ROBINS60238.2024.10534007(621-626)Online publication date: 17-Apr-2024
https://doi.org/10.1109/ICC-ROBINS60238.2024.10534007
Gioacchini LSantos WLopes BDrago IMellia MAlmeida JGonçalves M(2024)Explainable Stacking Models based on Complementary Traffic Embeddings2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00035(261-272)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00035
Khani PMoeinaddini EAbnavi NShahraki A(2024)Explainable artificial intelligence for feature selection in network traffic classificationTransactions on Emerging Telecommunications Technologies10.1002/ett.497035:4Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1002/ett.4970
Towhid MShahriar N(2022)Encrypted Network Traffic Classification using Self-supervised Learning2022 IEEE 8th International Conference on Network Softwarization (NetSoft)10.1109/NetSoft54395.2022.9844044(366-374)Online publication date: 27-Jun-2022
https://doi.org/10.1109/NetSoft54395.2022.9844044
Li JMishra DKrishnaswamy DChakraborty ADavis JSeneviratne A(2022)WiFi Interference-Based Adversarial Attacks on NTC Using CSI SensingICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9838755(4354-4359)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9838755
Lohrasbinasab IShahraki ATaherkordi ADelia Jurcut A(2022)From statistical‐ to machine learning‐based network traffic predictionTransactions on Emerging Telecommunications Technologies10.1002/ett.439433:4Online publication date: 17-Apr-2022
https://dl.acm.org/doi/10.1002/ett.4394
Biswas DNeuwirth SPaul AButt A(2021)Bridging Network and Parallel I/O Research for Improving Data-Intensive Distributed Applications2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS)10.1109/INDIS54524.2021.00011(50-56)Online publication date: Nov-2021
https://doi.org/10.1109/INDIS54524.2021.00011

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