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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking

C Labesh Kumar1, Suresh Betam2, Denis Pustokhin3

  • 1Department of Mechanical Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India.

Scientific Reports
|April 24, 2025
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Summary
This summary is machine-generated.

This study introduces a new Cybersecurity in Software-Defined Networking utilizing Hybrid Deep Learning Models and a Binary Narwhal Optimizer (CSSDN-HDLBNO) approach to combat Distributed Denial of Service (DDoS) attacks. The novel method achieves 99.40% accuracy in detecting these threats within Software-Defined Networking environments.

Keywords:
Binary Narwhal optimizerCybersecurityDeep learningDistributed denial of serviceSoftware-Defined networking

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Area of Science:

  • Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • Software-Defined Networking (SDN) centralizes control, creating vulnerabilities to cyber threats like Distributed Denial of Service (DDoS) attacks.
  • Deep Learning (DL) models, while powerful for intrusion detection, can be susceptible to adversarial attacks, leading to misclassification.
  • Existing Network Intrusion Detection Systems (NIDS) require enhancement to effectively counter sophisticated cyber threats in dynamic SDN environments.

Purpose of the Study:

  • To develop a novel Cybersecurity in Software-Defined Networking utilizing Hybrid Deep Learning Models and a Binary Narwhal Optimizer (CSSDN-HDLBNO) approach.
  • To provide a scalable and effective solution for safeguarding SDN environments against evolving cyber threats, specifically DDoS attacks.
  • To improve the accuracy and robustness of cyber threat detection in SDN.

Main Methods:

  • Data normalization using min-max scaling.
  • Feature selection using Binary Narwhal Optimizer (BNO).
  • DDoS attack classification employing Convolutional Neural Network with Bidirectional Gated Recurrent Units and Attention Mechanism (CNN-BiGRU-AM).
  • Hyperparameter tuning using Seagull Optimization Algorithm (SOA).

Main Results:

  • The CSSDN-HDLBNO approach demonstrated superior performance in detecting DDoS attacks within an SDN dataset.
  • Achieved a high accuracy of 99.40%, outperforming existing models across various evaluation metrics.
  • Validated the effectiveness and robustness of the proposed hybrid deep learning and optimization technique.

Conclusions:

  • The CSSDN-HDLBNO approach offers a significant advancement in securing SDN environments against DDoS attacks.
  • Hybrid deep learning models, combined with advanced optimization techniques, provide a robust framework for network intrusion detection.
  • The study highlights the potential of integrating novel optimization algorithms with deep learning for enhanced cybersecurity solutions.