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A novel network traffic classification approach via discriminative feature learning

Published: 30 March 2020 Publication History

Abstract

Network traffic classification plays an important role in many network monitoring and security tasks. More recently, with the development of deep learning techniques, the performance of network traffic classification has been significantly improved due to the powerful feature representations learned by deep neural networks. Despite the great success that has been achieved, the problems of within-class diversity and between-class similarity are still big challenges. In this paper, we propose to train a CNN model by optimizing a new discriminative objective function, where apart from minimizing the empirical risk, a metric learning regularization term is also imposed on the learned features. This metric learning regularization term enforces the CNN model to learn more discriminative features in the mapped feature space, where the instances from the same class are closer together while the instances of different classes are farther apart. We conduct extensive experiments to evaluate the proposed method on three traffic datasets. The experimental results demonstrate that our proposed method outperforms the existing baseline methods and obtains state-of-the-art results on all the three datasets.

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  • (2024)Considerations of Using the Network Traffic Dataset for Machine Learning Algorithms2024 15th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC62082.2024.10827554(427-428)Online publication date: 16-Oct-2024
  • (2024)Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral AnalysisAISMA-2023: International Workshop on Advanced Information Security Management and Applications10.1007/978-3-031-77229-0_8(74-84)Online publication date: 17-Nov-2024
  • (2023)BehavSniffer: Sniff User Behaviors from the Encrypted Traffic by Traffic Burst Graphs2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON58729.2023.10287511(456-464)Online publication date: 11-Sep-2023
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      cover image ACM Conferences
      SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing
      March 2020
      2348 pages
      ISBN:9781450368667
      DOI:10.1145/3341105
      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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      Published: 30 March 2020

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      Author Tags

      1. convolutional neural network (CNN)
      2. deep learning
      3. metric learning
      4. traffic classification

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      SAC '20
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      SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing
      March 30 - April 3, 2020
      Brno, Czech Republic

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      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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      View all
      • (2024)Considerations of Using the Network Traffic Dataset for Machine Learning Algorithms2024 15th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC62082.2024.10827554(427-428)Online publication date: 16-Oct-2024
      • (2024)Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral AnalysisAISMA-2023: International Workshop on Advanced Information Security Management and Applications10.1007/978-3-031-77229-0_8(74-84)Online publication date: 17-Nov-2024
      • (2023)BehavSniffer: Sniff User Behaviors from the Encrypted Traffic by Traffic Burst Graphs2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON58729.2023.10287511(456-464)Online publication date: 11-Sep-2023
      • (2022)A Survey of Network Traffic Classification Methods Using Machine LearningProgramming and Computing Software10.1134/S036176882207005248:7(413-423)Online publication date: 29-Nov-2022
      • (2022)Hierarchical Association Features Learning for Network Traffic Recognition2022 International Conference on Information Processing and Network Provisioning (ICIPNP)10.1109/ICIPNP57450.2022.00035(129-133)Online publication date: Sep-2022
      • (2022)Network traffic classification using convolutional neural network and ant-lion optimizationComputers and Electrical Engineering10.1016/j.compeleceng.2022.108024101:COnline publication date: 1-Jul-2022
      • (2022)Traffic Classification Based on CNN-LSTM Hybrid NetworkDigital TV and Wireless Multimedia Communications10.1007/978-981-19-2266-4_31(401-411)Online publication date: 17-Apr-2022
      • (2021)Towards Open World Traffic ClassificationInformation and Communications Security10.1007/978-3-030-86890-1_19(331-347)Online publication date: 17-Sep-2021
      • (2020)A Malware Identification Approach Based on Improved SVM in Network Traffic2020 7th International Conference on Dependable Systems and Their Applications (DSA)10.1109/DSA51864.2020.00012(37-40)Online publication date: Nov-2020

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