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Related Experiment Video

Updated: Sep 9, 2025

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Multilayered SDN security with MAC authentication and GAN-based intrusion detection.

Nanavath Kiran Singh Nayak1, Budhaditya Bhattacharyya1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

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|September 4, 2025
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Summary
This summary is machine-generated.

This study introduces a novel intrusion detection system for software-defined networks (SDN) using a four-Q curve authentication and deep learning. The system significantly enhances network security by accurately identifying and preventing cyberattacks with high precision and low false positives.

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Area of Science:

  • Cybersecurity
  • Computer Networks
  • Deep Learning

Background:

  • Software-defined networks (SDN) are increasingly targeted by cyberattacks due to their role in 5G data transmission.
  • Existing intrusion detection systems often suffer from low accuracy and high false positive rates, necessitating advanced solutions.

Purpose of the Study:

  • To develop a robust, multilayered intrusion detection system for SDN environments.
  • To enhance network security and efficiency through advanced authentication and deep learning techniques.

Main Methods:

  • Implementation of a novel four-Q curve authentication system utilizing elliptic curve cryptography for secure and efficient authentication.
  • Application of univariate ensemble feature selection for optimal switch selection.
  • Utilizing a Dual Discriminator Conditional Generative Adversarial Network (DDcGAN), optimized by the Sheep Flock Optimization Algorithm (SFOA), for classifying network traffic.
  • Employing the Growing Self-Organizing Map (GSOM) for categorizing suspicious packets.

Main Results:

  • The DDcGAN-based intrusion detection system achieved a high accuracy of 98.29% and an F1 score of 0.975.
  • Demonstrated superior performance over state-of-the-art methods in precision, sensitivity, and reduced false-positive rates (2.05%).
  • Achieved a true positive rate of 99.04% even with 50% malicious nodes and reported 4.5% energy savings.

Conclusions:

  • The proposed four-Q curve authentication and DDcGAN-based intrusion detection system offers a significant advancement in securing SDN environments.
  • The system effectively balances high detection accuracy with computational efficiency and reduced energy consumption.
  • This research provides a promising framework for future cybersecurity solutions in advanced network infrastructures.