A Novel Approach for Detection of DDoS Attacks in Software-Defined Networks Based on Grey Wolf Optimizer and Support Vector Machine

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 3

Year of Publication : 2024

Authors : Aminata Dembele, Elijah Mwangi, Kennedy K. Ronoh, Edwin O. Ataro

10.14445/23488379/IJEEE-V11I3P107

How to Cite?

Aminata Dembele, Elijah Mwangi, Kennedy K. Ronoh, Edwin O. Ataro, "A Novel Approach for Detection of DDoS Attacks in Software-Defined Networks Based on Grey Wolf Optimizer and Support Vector Machine," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 3, pp. 86-96, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I3P107

Abstract:

Software-defined networks face attacks that hinder efficient network provision and prevent users from accessing systems. Attack detection is crucial for better service provision and system resilience. Existing SDN-based Distributed Denial of Service (DDoS) detection technologies suffer from low accuracy, which is attributed to inadequate feature extraction and resultsin elevated false negative rates. This study introduces a solution leveraging the Grey Wolf Optimizer algorithm for feature selection to enhance DDoS attack detection and categorization. Employing a novel binary Grey Wolf optimization and Support Vector Machine (SVM) classifier on the InSDN dataset for SDNs, the proposed approach demonstrates superior performance, achieving 100% accuracy and recall. Feature selection with Binary Grey Wolf yields a 97% F1-Score using the unimodal equation and 100% accuracy, 96% recall, and a 98% F1-Score with the multi-modal equation, underscoring its efficacy in bolstering SDN security against DDoS attacks.

Keywords:

Intrusion Detection System, SDN, DDoS attack, SVM, Grey Wolf Optimizer, Binary Grey Wolf Optimizer, InSDN dataset.

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