Analyzing ML/DL Techniques for SDN-Based DDoS Attack Detection: A Comparative Study

Authors

  • Hadeil Elshaik School of Electronic Engineering, College of Engineering, Sudan University of Science and Technology
  • Salaheldin Edam School of Electronic Engineering, College of Engineering, Sudan University of Science and Technology

Abstract

An abstract is This study conducts a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) techniques for detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments. Utilizing a diverse and representative dataset with real-world traffic patterns and various DDoS attack scenarios, we evaluate ML algorithms (SVM, Decision Trees, Random Forest, k-NN) and DL models (CNN, LSTM, GRU) for SDN-based DDoS detection. Results indicate that deep learning models, particularly CNN, LSTM, and GRU, outperform traditional ML algorithms in accuracy, precision, recall, F1-score, and AUC-ROC. CNN achieves the highest accuracy (97%) and AUC-ROC (99%), making it the most effective approach. SDN-specific considerations reveal that all selected algorithms adapt well to dynamic SDN environments. While deep learning models incur higher computational overhead, their performance benefits justify the additional computation, making them viable for practical deployment. This study recommends CNN as the top choice for SDN-based DDoS detection, with LSTM and GRU as strong alternatives. SVM and Random Forest are suitable for resource-constrained environments, while k-NN and Decision Trees may serve specific use cases

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Published

2024-01-03

Issue

Vol. 1 No. 2 (2023): Journal of Intelligent Systems and applied data science (JISADS)

Section

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