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Anomaly Detection Module for Network Traffic Monitoring in Public Institutions.

Łukasz Wawrowski1, Andrzej Białas1, Adrian Kajzer2

  • 1Łukasiewicz Research Network-Institute of Innovative Technologies EMAG, ul. Leopolda 31, 40-189 Katowice, Poland.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an anomaly detection module for enhanced network traffic safety in public institutions. Combined models achieved 100% accuracy in detecting specific network attacks, improving cybersecurity.

Keywords:
anomaly detectioncybersecuritynetwork traffic monitoring

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Increasing attention to network traffic safety is crucial.
  • Existing approaches can be enhanced through continuous monitoring and anomaly detection.
  • Public institutions require robust network security services.

Purpose of the Study:

  • To develop an anomaly detection module for improving network traffic safety.
  • To provide public institutions with an advanced network security component.
  • To introduce a novel strategy for model selection and tuning in anomaly detection.

Main Methods:

  • Continuous monitoring of network traffic statistics.
  • Detection of anomalies in network traffic descriptions.
  • Development of a strategy for selecting and tuning combined anomaly detection models.
  • Offline model tuning for faster deployment.

Main Results:

  • The developed anomaly detection module offers an enhanced approach to network traffic safety.
  • A novel strategy for selecting and tuning combined models was implemented.
  • Combined models achieved 100% balanced accuracy in detecting specific network attacks.
  • The module is designed as an additional component for public institution network security services.

Conclusions:

  • The anomaly detection module significantly enhances network traffic safety.
  • The novel model selection and tuning strategy leads to high detection accuracy.
  • This solution is particularly beneficial for public institutions seeking advanced cybersecurity.
  • Achieving 100% balanced accuracy in attack detection demonstrates the module's effectiveness.