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An Edge-Computing-Based Integrated Framework for Network Traffic Analysis and Intrusion Detection to Enhance

Tamara Zhukabayeva1,2, Zulfiqar Ahmad3, Aigul Adamova1,2

  • 1Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan.

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Summary

This study proposes an Industrial Internet of Things (IIoT) cybersecurity framework using edge computing and machine learning for real-time network traffic analysis and intrusion detection. The framework effectively identifies threats, enhancing IIoT security.

Keywords:
clusteringcybersecuritycyber–physical systemsindustrial IoTintrusion detection and preventionmachine learningnetwork analysis

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Industrial Internet of Things (IIoT) environments face increasing security challenges due to escalating network traffic and connectivity.
  • Implementing robust security measures is crucial for the reliable operation of IIoT systems.

Purpose of the Study:

  • To develop and evaluate a novel framework for network traffic analysis and intrusion detection in IIoT environments.
  • To leverage edge computing and machine learning techniques for enhanced cybersecurity.

Main Methods:

  • Utilized k-means and DBSCAN clustering for network traffic analysis and anomaly detection.
  • Employed k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) for intrusion detection.
  • Integrated edge computing for real-time data processing and reduced latency.

Main Results:

  • K-means (silhouette score 0.612) outperformed DBSCAN (0.473) in traffic behavior clustering.
  • Random Forest and KNN demonstrated high precision, recall, and F1 scores for intrusion detection.
  • The proposed framework with edge computing achieved real-time processing and lower security system latency.

Conclusions:

  • The developed framework effectively enhances IIoT cybersecurity by providing real-time threat identification.
  • The integration of edge computing and machine learning offers tangible improvements over existing security approaches.
  • This research contributes valuable insights into securing complex IIoT ecosystems.