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Explainable Security in SDN-Based IoT Networks.

Alper Kaan Sarica1, Pelin Angin1

  • 1Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.

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Summary

This study introduces a real-time intrusion detection and mitigation system for Software-Defined Networking (SDN) in Internet of Things (IoT) environments. The solution uses automated flow analysis and random forest classifiers for autonomous, accurate network security.

Keywords:
5GIoTSDNintrusion detectionmachine learningsecurity

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Area of Science:

  • Computer Science
  • Network Security
  • Internet of Things (IoT)

Background:

  • Wireless network advancements have enabled numerous Internet of Things (IoT) applications.
  • Future networks (5G and beyond) will heavily utilize Software-Defined Networking (SDN) and Network Functions Virtualization (NFV).
  • The expanding IoT landscape presents a large attack surface, necessitating robust security measures.

Purpose of the Study:

  • To propose a real-time intrusion detection and mitigation solution for SDN-based IoT networks.
  • To achieve autonomous security with high interpretability for human experts in 5G and beyond IoT environments.
  • To address the need for intelligent, automated security in high-traffic IoT networks.

Main Methods:

  • Development of an automated flow feature extraction and classification system.
  • Implementation of random forest classifiers at the SDN application layer.
  • Generation of a specific SDN dataset tailored for IoT security analysis.

Main Results:

  • Demonstrated high accuracy in real-time intrusion detection within SDN-managed IoT networks.
  • Evaluated the performance impact of the proposed security mechanism.
  • The proposed approach shows promise for effective attack detection and mitigation.

Conclusions:

  • The developed SDN security solution is effective for real-time, automated intrusion detection and mitigation in IoT.
  • The approach offers a promising path towards securing high-traffic 5G and beyond IoT networks.
  • High accuracy and interpretability are key features of the proposed system.