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A Concept Drift Detection Approach Based on Jensen-Shannon Divergence for Network Traffic Classification

Published: 16 May 2023 Publication History

Abstract

Network traffic features change with time and network environment, creating a concept drift problem that leads to a decrease in the accuracy of machine learning-based network traffic classification methods. This is because the traditional network traffic classifiers are static models that cannot adapt to the changes in data distribution. Therefore, we proposed a concept drift detection approach based on Jensen–Shannon divergence, named CDJD. The method uses a double-layer window mechanism to detect changes in data distribution based on the Jensen-Shannon divergence, and thus detects concept drift. After detecting concept drift, the Jensen-Shannon divergence is used to check whether the current concept is a recurrence of the past concept and thus decide whether to reuse the old classifier. The method is experimentally compared with common concept drift detection methods, and the experimental results show that the method can effectively detect concept drift and showing better classification performance.

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Cited By

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  • (2025)Domino drift effect approach for probability estimation of feature drift in high-dimensional dataKnowledge and Information Systems10.1007/s10115-025-02362-0Online publication date: 13-Feb-2025
  • (2024)Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated PreferencesACM Transactions on Information Systems10.1145/370663343:2(1-25)Online publication date: 5-Dec-2024

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  1. A Concept Drift Detection Approach Based on Jensen-Shannon Divergence for Network Traffic Classification

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    cover image ACM Other conferences
    AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
    September 2022
    1221 pages
    ISBN:9781450396899
    DOI:10.1145/3573942
    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: 16 May 2023

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    Author Tags

    1. Concept drift
    2. Jensen–Shannon divergence
    3. Machine learning
    4. Network traffic classification

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    View all
    • (2025)Domino drift effect approach for probability estimation of feature drift in high-dimensional dataKnowledge and Information Systems10.1007/s10115-025-02362-0Online publication date: 13-Feb-2025
    • (2024)Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated PreferencesACM Transactions on Information Systems10.1145/370663343:2(1-25)Online publication date: 5-Dec-2024

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