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Anonymous Traffic Detection Based on Feature Engineering and Reinforcement Learning

  • 0Faculty of Computer Engineering, Charles W. Davidson College of Engineering, San Jose State University, San Jose, CA 95192, USA.
Sensors (basel, Switzerland) +

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April 13, 2024

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April 13, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel reinforcement learning framework for detecting anonymous network traffic. The system achieves over 80% accuracy without needing labeled data, enhancing network security against suspicious activities.

Area Of Science

  • Computer Science
  • Network Security
  • Machine Learning

Background

  • Anonymous networks are crucial for user privacy but also facilitate malicious activities.
  • Detecting anonymous network traffic is essential for internet security against evolving threats.
  • Existing machine learning methods for detection often require extensive labeled datasets and complex models.

Purpose Of The Study

  • To develop an efficient and accurate system for detecting anonymous network traffic.
  • To overcome the limitations of existing methods, particularly the reliance on labeled data and complex architectures.
  • To improve the training efficiency and performance of anonymous traffic detection systems.

Main Methods

  • Utilized feature engineering to extract and rank pattern information from static traces of anonymous traffic.
  • Developed a reinforcement learning framework with states, actions, rewards, and state transitions.
  • Implemented a lightweight system for classifying anonymous and non-anonymous traffic using fine-tuned thresholds instead of traditional labels.

Main Results

  • The proposed system successfully identifies anonymous network traffic.
  • The system achieves an accuracy rate exceeding 80% based on pattern information.
  • The approach demonstrates effective anonymous traffic detection without requiring labeled data.

Conclusions

  • The developed reinforcement learning framework offers a promising solution for detecting anonymous network traffic.
  • The system's ability to perform detection without labeled data addresses a key challenge in the field.
  • This research contributes to enhancing network security by providing a more efficient and accurate detection mechanism.

Keywords: