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Related Experiment Videos

Enhancing network traffic detection via interpolation augmentation and contrastive learning.

Lei Li1, Qiang Zhou1, Xinlong Yang1

  • 1Ningbo University, College of Science and Technology, Ningbo, Zhejiang, China.

Plos One
|December 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network traffic detection method (TICL) using data interpolation and contrastive learning. TICL effectively addresses data imbalance and improves detection of coordinated cyberattacks in large-scale network environments.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Traditional network traffic detection methods lack global context, hindering multi-flow coordinated attack detection.
  • Real-world network traffic data exhibits significant imbalance, impairing model performance.
  • Existing methods struggle with detecting sophisticated, coordinated cyber threats.

Purpose of the Study:

  • To propose a novel network traffic detection method (TICL) overcoming limitations of traditional approaches.
  • To enhance detection performance against multi-flow coordinated attacks.
  • To address data imbalance issues in network traffic analysis.

Main Methods:

  • Utilized data interpolation techniques to generate negative samples, mitigating data imbalance.
  • Employed contrastive learning to capture distinctions between positive and negative samples.
  • Developed a Traffic Interpolation and Contrastive Learning (TICL) framework.

Main Results:

  • TICL effectively mitigated data imbalance problems in network traffic datasets.
  • Contrastive learning enhanced model generalization and improved detection accuracy.
  • Experimental results showed TICL significantly outperformed existing intrusion detection methods on large-scale datasets.

Conclusions:

  • The proposed TICL method offers a robust solution for network traffic detection.
  • TICL demonstrates strong potential for practical applications in cybersecurity.
  • This approach enhances the ability to detect complex, coordinated cyberattacks.