Publication

Abstract : SDN, short for Software Defined Networking, represents a network architecture controlled by software applications. Distributed Denial-of-Service (DDoS) attacks create a challenge to these SDN environments and lead to many issues like loss of data and network instability, and takes a lot of time to address requests made by human users. It is very crucial to detect such attacks, for users to seamlessly use the network and take advantage of what is available online. This study demonstrates the significance of Machine Learning algorithms in identifying malicious traffic. Employing algorithms Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) proves effective in detecting complex and dynamic attacks. The study utilizes Local Interpretable Model-agnostic Explanation (LIME) to analyze the strength of each feature that contributes towards the prediction of classification. The results highlight the superior performance of the Gradient Boosting Classifier, achieving an accuracy of 99.75% and an F1-score of 99.68% in detecting network anomalies and threats.