<![CDATA[In the rapidly evolving landscape of network management, software-defined networking (SDN) stands out as a transformative technology. It revolutionizes network management by decoupling the control and data planes, enhancing both flexibility and operational efficiency. However, this separation introduces significant security challenges, such as data interception, manipulation, and unauthorized access. To address these issues, this paper investigates the application of advanced clustering and classification algorithms for anomaly detection and traffic analysis in SDN environments. We present a novel approach that integrates multiple k-means clustering models with Word2Vec for feature extraction, followed by classification using a neural network (NN). Our method is rigorously benchmarked against a traditional NN model to comprehensively evaluate performance. Experimental results indicate that our approach outperforms the NN model, achieving an accuracy of 99.97% on the InSDN dataset and 98.65% on the CIC-DDoS2019 dataset, showcasing its effectiveness in detecting anomalies without relying on feature selection. These findings suggest that integrating clustering techniques with feature extraction algorithms can significantly enhance the security of SDN infrastructures.]]>
Copyrights © 2025
