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CDBC: A novel data enhancement method based on improved between-class learning for darknet detection.

Binjie Song1, Yufei Chang2, Minxi Liao3

  • 1Academy of A&AD, Zhengzhou 450000, China.

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Summary
This summary is machine-generated.

This study introduces Chebyshev distance based Between-class learning (CDBC) to improve darknet traffic detection. CDBC enhances accuracy and recall, addressing challenges posed by unbalanced datasets in cybersecurity.

Keywords:
between-class learningdarknetdetectiontraffic classification

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Area of Science:

  • Cybersecurity
  • Network Security
  • Data Science

Background:

  • The internet's growth and privacy concerns have led to widespread use of privacy protection technologies.
  • These technologies inadvertently facilitate the darknet, a platform exploited by criminals for economic and intelligence purposes.
  • Detecting darknet traffic is crucial but challenging due to seriously unbalanced data, hindering accuracy.

Purpose of the Study:

  • To address the difficulties in darknet traffic detection caused by unbalanced datasets.
  • To propose a novel learning method, Chebyshev distance based Between-class learning (CDBC), to improve detection accuracy.
  • To introduce an improved darknet traffic detection method leveraging CDBC.

Main Methods:

  • Proposed Chebyshev distance based Between-class learning (CDBC) to analyze darknet dataset spatial distribution.
  • Generated "gap data" using CDBC to optimize dataset distribution boundaries.
  • Developed and tested a novel darknet traffic detection method incorporating CDBC.

Main Results:

  • CDBC significantly improved the accuracy of over 10 existing methods, reaching up to 99.99% on benchmark datasets (ISCXTor 2016, CIC-Darknet 2020).
  • The proposed method demonstrated superior performance compared to other sampling techniques.
  • CDBC enhanced classifier recall, indicating more effective identification of darknet traffic.

Conclusions:

  • CDBC is an effective method for optimizing dataset distribution and improving darknet traffic detection.
  • The proposed detection method, enhanced by CDBC, offers a significant advancement in cybersecurity for identifying illicit online activities.
  • CDBC provides a valuable tool for enhancing the performance of various machine learning models in unbalanced traffic detection scenarios.