Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection

Abdulrahman A. Alshdadi (), Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami and Faisal S. Alsubaei
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Abdulrahman A. Alshdadi: Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Abdulwahab Ali Almazroi: College of Computing and Information Technology at Khulais, Department of Information Technology, University of Jeddah, Jeddah 21959, Saudi Arabia
Nasir Ayub: Department of Creative Technologeis, Air University Islamabad, Islamabad 44000, Pakistan
Miltiadis D. Lytras: Management of Information Systems Department, Deree College, The American College of Greece, 15342 Athens, Greece
Eesa Alsolami: Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Faisal S. Alsubaei: Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia

Future Internet, 2024, vol. 16, issue 12, 1-26

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.

Keywords: denial of service attack; deep learning ensembler; network layer security; transport layer security; IoT security; cyber security (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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