ItemArticle

Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm

Title
Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm
Creator
Barona, R.; Babu Raj, E.
Description
The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability of the current network, especially in Internet of Things (IoT) and cloud environments. Traditional detection methods often struggle with the inability to achieve a balance between detection accuracy and computational efficiency. In this manuscript, the Classification of Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized with Gazelle Optimization Algorithm (DDOS-AD-BWRF-GOA) is proposed. First, the raw data is gathered from the CICDDoS2019 dataset. Then, input data are preprocessed utilizing Adaptive Bitonic Filtering for normalizing the values. The preprocessed data are fed to the Improved Feed Forward Long Short-Term Memory technique for selecting features that increase the model's execution time. The selected features are supplied to the Bayesian Weighted Random Forest (BWRF), which classifies the multiclass DDOS attack. In general, Bayesian Weighted Random Forest does not adopt any optimization methods to define optimal parameters to guarantee exact DDOS identification. Hence, GOA is proposed to optimize the Bayesian Weighted Random Forest classifier. The proposed method is implemented in MATLAB. The performance metrics, such as Accuracy, Precision, Recall, F1-score, Specificity, Error rate, and Computational time are evaluated. The proposed method attains 15.34%, 24.1%, and 18.9% higher accuracy and 12.4%, 18.24%, and 22.6% higher precision when analyzed with existing techniques: Hybrid deep learning method for DDOS detection and classification (HDL-DDOS-DC), Edge-HetIoT Defense against DDoS attack utilizing learning techniques (EHD-DDOS-LT), and Digital twin-enabled intelligent DDOS detection for autonomous core networks (DTI-DDOS-ACN), respectively. 2025 John Wiley & Sons Ltd.
Source
Transactions on Emerging Telecommunications Technologies;Volume;36;Issue;4;Article No.;e70092;
Date
01-01-2025
Publisher
John Wiley and Sons Inc
Subject
adaptive bitonic filtering; bayesian weighted random forest; gazelle optimization algorithm; improved feed forward-long short-term memory method; raw data
Coverage
Barona R., Department of Computer Science and Engineering, St. Xavier's Catholic College of Engineering, Nagercoil, India; Babu Raj E., Department of Computer Science and Engineering, Christ University, Bengaluru, India
Rights
All Open Access; Bronze Open Access
Relation
ISSN: 21613915;
Format
online
Language
English
Type
Article

Citation

Barona, R.; Babu Raj, E., “Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/21773.

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