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Identifying Hybrid DDoS Attacks in Deterministic Machine-to-Machine Networks on a Per-Deterministic-Flow Basis.

Yen-Hung Chen1, Yuan-Cheng Lai2, Kai-Zhong Zhou2

  • 1Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112, Taiwan.

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Summary

This study introduces a Flow Differentiation Detector (FDD) to combat hybrid DDoS attacks in Deterministic Networks (DetNet). The FDD achieves over 90% accuracy in detecting these sophisticated attacks on 5G and 6G networks.

Keywords:
DetNetHybrid DDoS attacksSDNflow-based detection

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Area of Science:

  • Network Security
  • Cybersecurity
  • Telecommunications Engineering

Background:

  • Deterministic Networks (DetNet) are crucial for 5G/6G, but resource sharing with conventional traffic creates hybrid DDoS vulnerabilities.
  • Existing DDoS detection methods fail against hybrid attacks targeting multiple DetNet components (controllers and links) simultaneously.
  • Evolving cyber threats necessitate advanced security solutions for latency-sensitive data in next-generation networks.

Purpose of the Study:

  • To propose a novel Flow Differentiation Detector (FDD) for identifying hybrid Distributed Denial of Service (DDoS) attacks in DetNet environments.
  • To develop a robust system capable of detecting simultaneous attacks on both DetNet controllers and network links.
  • To enhance the security posture of 5G and 6G networks against sophisticated, multi-target cyber threats.

Main Methods:

  • Implementation of a fuzzy-based Target Link Selection mechanism to identify critical network links vulnerable to attack.
  • Statistical evaluation of traffic patterns across selected links to differentiate between normal and malicious flows.
  • Deployment of the Flow Differentiation Detector (FDD) within the OpenDayLight Software-Defined Networking (SDN) controller.

Main Results:

  • The FDD demonstrated superior detection accuracy, exceeding 90%, compared to traditional DDoS detection methods.
  • Effective detection was achieved across various hybrid DDoS attack ratios, network topologies, and scales.
  • The system successfully identified complex attacks targeting multiple network elements concurrently.

Conclusions:

  • The proposed Flow Differentiation Detector (FDD) offers a highly accurate and effective solution for hybrid DDoS attack detection in DetNet.
  • The FDD enhances the security of 5G and 6G networks by addressing the limitations of previous detection methodologies.
  • This research provides a practical implementation within an SDN controller, paving the way for more resilient future networks.