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Gauss Markov and Flow Balanced Vector Radial Learning network traffic classification on IoT with SDN.

Rajkumar Kulandaivel1, Manikandan Ramachandran1, Sathishkumar Veerappampalayam Easwaramoorthy2

  • 1School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.

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This study introduces a new method for classifying network traffic from Internet of Things (IoT) devices using Software-Defined Networking (SDN). The Gauss Markov and Flow-balanced Vector Radial Learning (GM-FVRL) technique improves accuracy and reduces latency in IoT networks.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Machine Learning

Background:

  • The proliferation of connected devices (Internet of Things - IoT) has led to a significant increase in network traffic, demanding efficient resource allocation and classification.
  • Traditional methods struggle to accurately categorize IoT network traffic and manage resources, leading to issues with accuracy and latency.
  • Software-Defined Networking (SDN) offers a promising solution by enabling advanced techniques like Machine Learning (ML) for network automation.

Purpose of the Study:

  • To propose a novel network traffic classification technique for IoT environments leveraging SDN.
  • To address the limitations of conventional methods in handling the complexity and volume of IoT network traffic.
  • To enhance network performance by improving classification accuracy and minimizing latency.

Main Methods:

  • Development of a new technique named Gauss Markov and Flow-balanced Vector Radial Learning (GM-FVRL).
  • Utilizing SDN to extract relevant network traffic features from IoT devices via Gauss Markov Correlation-based IoT Network Traffic Feature Extraction.
  • Employing a flow-balanced radial-based ML model for traffic categorization, which mitigates noise from distinct network flows.

Main Results:

  • The proposed GM-FVRL method demonstrated high classification accuracy in identifying network traffic from IoT devices.
  • The technique significantly minimized network latency, leading to improved overall network performance.
  • Enhanced precision and recall were achieved, indicating a more reliable and effective traffic classification system.

Conclusions:

  • The GM-FVRL technique effectively classifies network traffic in IoT environments integrated with SDN.
  • The method successfully improves accuracy and reduces latency, outperforming conventional approaches.
  • GM-FVRL ensures better precision and recall, making it a valuable solution for modern network management challenges.