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Human-guided auto-labeling for network traffic data: The GELM approach.

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Summary

This study introduces a human-guided auto-labeling algorithm for efficient data labeling, particularly in network security. The method ensures quick, accurate, and consistent labeling, proving effective for datasets with specific attack types like DDoS.

Keywords:
Attack predictionAuto-labeling processGeneralized extreme learning machineHuman-guided labelingMoore–Penrose generalized inverseNetwork traffic

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Accurate data labeling is essential for supervised learning and classification in network security.
  • Existing methods may lack efficiency and consistency in labeling large datasets.

Purpose of the Study:

  • To develop a human-guided auto-labeling algorithm using self-supervised learning for fast, accurate, and consistent data labeling.
  • To evaluate the algorithm's performance on network traffic datasets, including various cyberattacks.

Main Methods:

  • A three-process algorithm: auto-labeling with weighted features, validation using Generalized Extreme Learning Machine (GELM), and an update process for new data.
  • Application to five network traffic datasets, including Distributed Denial of Service (DDoS), DoS, BruteForce, and PortScan attacks.

Main Results:

  • The algorithm demonstrated quick, accurate, and consistent labeling of unlabeled datasets.
  • Generalized Extreme Learning Machine (GELM) enabled real-time data labeling.
  • High performance similarity between auto-labels and conventional labels was observed on DDoS-only datasets.
  • Performance differences were noted on datasets with diverse attack types, suggesting areas for further investigation.

Conclusions:

  • The human-guided auto-labeling algorithm offers a viable criterion for real-time data labeling in various applications, especially network security.
  • Further research is needed to address performance variations on complex, multi-attack datasets.