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A few-shot network intrusion detection method based on mutual centralized learning.

Congyuan Xu1,2, Fan Zhang3, Ziqi Yang4

  • 1College of Information Science and Engineering, Jiaxing University, Jiaxing, 314001, China.

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This study introduces a novel few-shot network intrusion detection (FS-MCL) method to improve performance with limited data. The approach enhances intrusion detection accuracy, even with scarce network traffic datasets.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Deep learning models for intrusion detection require extensive training data.
  • Few-shot network traffic presents a significant challenge, leading to suboptimal detection rates.
  • Existing methods struggle with data scarcity in network intrusion detection.

Purpose of the Study:

  • To propose a novel few-shot network intrusion detection method (FS-MCL) to overcome data limitations.
  • To enhance the detection performance for network intrusion with limited available data.
  • To develop a method effective for few-shot learning scenarios in network security.

Main Methods:

  • Proposed a few-shot network intrusion detection method based on mutual centralized learning (FS-MCL).
  • Utilized dense features extracted by an encoder and associated them with particles in discrete space.
  • Employed a Markov process to measure expected visits of dense features for classification probability.
  • Developed a visualization technique to convert network traffic into image-like data for dataset construction.

Main Results:

  • The FS-MCL method demonstrated excellent binary and multi-classification performance.
  • Achieved an average detection rate of up to 99.84% in experiments.
  • Effectively addressed the challenge of few-shot network traffic detection.

Conclusions:

  • The proposed FS-MCL method is effective for few-shot network intrusion detection.
  • The visualization technique aids in creating usable datasets from limited network traffic.
  • This approach significantly improves detection rates in data-scarce environments.