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Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection

Author

Listed:
  • Abdulrahman A. Alshdadi

    (Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
    These authors contributed equally to this work.)

  • Abdulwahab Ali Almazroi

    (College of Computing and Information Technology at Khulais, Department of Information Technology, University of Jeddah, Jeddah 21959, Saudi Arabia
    These authors contributed equally to this work.)

  • Nasir Ayub

    (Department of Creative Technologeis, Air University Islamabad, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Miltiadis D. Lytras

    (Management of Information Systems Department, Deree College, The American College of Greece, 15342 Athens, Greece
    These authors contributed equally to this work.)

  • Eesa Alsolami

    (Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
    These authors contributed equally to this work.)

  • Faisal S. Alsubaei

    (Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia)

Abstract

The increasing threat of Distributed DDoS attacks necessitates robust, big data-driven methods to detect and mitigate complex Network and Transport Layer (NTL) attacks. This paper proposes EffiGRU-GhostNet, a deep-learning ensemble model for high-accuracy DDoS detection with minimal resource consumption. EffiGRU-GhostNet integrates Gated Recurrent Units (GRU) with the GhostNet architecture, optimized through Principal Component Analysis with Locality Preserving Projections (PCA-LLP) to handle large-scale data effectively. Our ensemble was tested on IoT-23, APA-DDoS, and additional datasets created from popular DDoS attack tools. Simulations demonstrate a recognition rate of 98.99% on IoT-23 with a 0.11% false positive rate and 99.05% accuracy with a 0.01% error on APA-DDoS, outperforming SVM, ANN-GWO, GRU-RNN, CNN, LSTM, and DBN baselines. Statistical validation through Wilcoxon and Spearman’s tests further verifies EffiGRU-GhostNet’s effectiveness across datasets, with a Wilcoxon F-statistic of 7.632 ( p = 0.022) and a Spearman correlation of 0.822 ( p = 0.005). This study demonstrates that EffiGRU-GhostNet is a reliable, scalable solution for dynamic DDoS detection, advancing the field of big data-driven cybersecurity.

Suggested Citation

  • Abdulrahman A. Alshdadi & Abdulwahab Ali Almazroi & Nasir Ayub & Miltiadis D. Lytras & Eesa Alsolami & Faisal S. Alsubaei, 2024. "Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection," Future Internet, MDPI, vol. 16(12), pages 1-26, December.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:458-:d:1536917
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