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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
S., Neethu
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Computer Science & IT Engineering

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Evaluation of distributed denial of service attacks detection in software defined networks S., Neethu; Aradhya, H. V. Ravish
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4488-4498

Abstract

Software-defined networking (SDN) revolutionizes networking by separating control logic and data forwarding, enhancing security against threats like distributed denial of service (DDoS) attacks. These attacks flood control plane bandwidth, causing SDN network failures. Recent studies emphasize the efficacy of machine learning (ML) and statistical approaches in identifying and mitigating these security risks. However, there has been a lack of focus on employing ensembling techniques, amalgamating diverse ML models, selecting pertinent features, and utilizing oversampling techniques to balance categorical data. Our study evaluates 20 machine-learning models, emphasizing feature engineering and addressing class imbalance using synthetic minority oversampling technique (SMOTE). The results indicate that ensemble methods such as light gradient boosting machine (LGBM) classifier, random forest classifier, XGB classifier, decision tree classifier obtained near-perfect scores (almost 100%) across all metrics, suggesting potential overfitting. Conversely, models like AdaBoost classifier, k-neighbors classifier, and support vector classifier (SVC) exhibited slightly lower (99%) but realistic performance, underscoring the intricacy of accurate prediction in cybersecurity. Simpler models, including logistic regression, linear discriminant analysis, and Gaussian naive Bayes, demonstrated moderate to low accuracy, approximately around 70%. These findings stress the imperative need for a nuanced approach in the selection and fine-tuning of ML models to ensure effective DDoS detection in SDN environments. 
Deep learning-based evaluation for distributed denial of service attacks detection S., Neethu; Aradhya, H. V. Ravish; Reddy Karna, Viswavardhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4982-4992

Abstract

Software-defined network (SDN) introduces a programmable and centralized control mechanism for managing network infrastructure, enhancing flexibility and efficiency. However, this architecture is prone to security threats, particularly distributed denial of service (DDoS) attacks that exploit centralized control. This study presents a comparative analysis of several deep learning (DL) models—namely, multilayer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)—for detecting DDoS threats within SDN environments. The research incorporates key preprocessing techniques such as feature selection and synthetic minority oversampling technique (SMOTE) to handle class imbalance. The results indicate that sequence-aware models like LSTM and RNN are highly effective in interpreting temporal network behavior, with LSTM achieving the highest performance (accuracy: 91%, precision: 86%, recall: 94%, and F1-score: 90%). These findings underscore the potential of advanced DL methods in fortifying SDN infrastructures against complex cyber threats.
Co-Authors Aradhya, H. V. Ravish Reddy Karna, Viswavardhan