http://drr.vau.ac.lk/handle/123456789/257 2026-05-29T16:01:36Z 2026-05-29T16:01:36Z http://drr.vau.ac.lk/handle/123456789/2069 2026-05-11T11:23:32Z 2023-01-01T00:00:00Z

Good Practices for Food-Manufacturing Businesses

2023-01-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/2068 2026-05-11T11:18:58Z 2023-01-01T00:00:00Z

University Handbook of Disability University of Vavuniya

2023-01-01T00:00:00Z Douglas, S. http://drr.vau.ac.lk/handle/123456789/2061 2026-04-27T08:06:43Z 2026-01-28T00:00:00Z

DGAA’s Tami Pronouncing Dictionary Douglas, S.

2026-01-28T00:00:00Z Karunarathne, K.M.G.B.C. Dissanayaka, D.M.H.V. Senanayake, P.S.R.P.S. Mayuran, P. Senthooran, V. http://drr.vau.ac.lk/handle/123456789/2038 2026-03-26T04:08:43Z 2026-01-01T00:00:00Z

Flow-Based Ensemble Learning for Intrusion Detection in Software-Defined Networks Karunarathne, K.M.G.B.C.; Dissanayaka, D.M.H.V.; Senanayake, P.S.R.P.S.; Mayuran, P.; Senthooran, V. We propose a machine learning-based intrusion detection system for SDN, considering the special vulnerability of the centralized control plane. It is trained on a publicly available dataset for SDN network traffic, which includes flow attributes such as the number of packets, the number of bytes, the flow duration, and the packet rate. To ensure the robustness of the learning process, the dataset is subjected to preprocessing techniques such as class balancing using SMOTE, feature scaling, and cross-validation. The proposed IDS model employs supervised learning techniques such as Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) for the detection of intrusions. Among these, ensemble-based models such as Random Forest and XGBoost show promising results, with an accuracy of 99% for the detection of intrusions in SDNs. All the models show high precision and recall, with XGBoost being the best choice in terms of performance and efficiency. From the experimental results, it is clear that the proposed model for intrusion detection in SDNs is effective, scalable, and viable for the security of the SDN infrastructure without compromising the performance of the network, thus making it suitable for real-time applications.We propose a machine learning-based intrusion detection system for SDN, considering the special vulnerability of the centralized control plane. It is trained on a publicly available dataset for SDN network traffic, which includes flow attributes such as the number of packets, the number of bytes, the flow duration, and the packet rate. To ensure the robustness of the learning process, the dataset is subjected to preprocessing techniques such as class balancing using SMOTE, feature scaling, and cross-validation. The proposed IDS model employs supervised learning techniques such as Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) for the detection of intrusions. Among these, ensemble-based models such as Random Forest and XGBoost show promising results, with an accuracy of 99% for the detection of intrusions in SDNs. All the models show high precision and recall, with XGBoost being the best choice in terms of performance and efficiency. From the experimental results, it is clear that the proposed model for intrusion detection in SDNs is effective, scalable, and viable for the security of the SDN infrastructure without compromising the performance of the network, thus making it suitable for real-time applications.

2026-01-01T00:00:00Z