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Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning.

Ibrahim A Alwhbi1, Cliff C Zou1, Reem N Alharbi1

  • 1Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning enhances encrypted traffic analysis and classification, crucial for network security. This survey details methods for understanding diverse network patterns and identifies future research directions.

Keywords:
device fingerprintingencrypted network trafficmachine learningtraffic classification

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Area of Science:

  • Cybersecurity
  • Network Engineering
  • Machine Learning

Background:

  • Encryption is vital for data confidentiality but complicates traditional network traffic inspection.
  • The rise of diverse network traffic (IoT, web, mobile) necessitates advanced analysis and classification methods.
  • Understanding encrypted traffic is critical for network administrators, cybersecurity experts, and policy enforcers.

Purpose of the Study:

  • To provide a comprehensive survey of machine learning applications in encrypted traffic analysis.
  • To detail the procedures and methodologies for using machine learning in this domain.
  • To review state-of-the-art techniques and explore future research avenues in encrypted traffic classification.

Main Methods:

  • Literature review of recent advancements in machine learning-driven encrypted traffic analysis.
  • Detailed explanation of machine learning procedures for traffic analysis and classification.
  • Categorization and discussion of current state-of-the-art techniques and methodologies.

Main Results:

  • Identified key machine learning approaches for analyzing and classifying encrypted network traffic.
  • Summarized current techniques and methodologies employed in the field.
  • Highlighted the challenges and opportunities in machine learning-based encrypted traffic analysis.

Conclusions:

  • Machine learning offers powerful tools for understanding and classifying encrypted traffic.
  • The survey provides a roadmap for current practices and future research in this critical cybersecurity area.
  • Effective analysis of encrypted traffic is essential for maintaining network security and integrity.