Intelligent Intrusion Detection in Software-Defined Networking: A Comparative Study of SVM and ANN Models

Abstract

Software-defined networking (SDN) has emerged as a promising approach for managing network infrastructure through a centralized controller. However, the dynamic nature of SDN makes it susceptible to security threats, including DoS and DDoS attacks. Intrusion detection systems (IDS) based on machine learning (ML) can efficiently detect and mitigate these attacks. This study compares two ML models, namely support vector machines (SVM) and artificial neural networks (ANN), for intelligent intrusion detection in SDN. To assess the performance of the ML models, we utilized the NSL-KDD dataset, with a specific emphasis on DDoS attacks, and compared their accuracy (Acc), precision, recall, and F1-score metrics. The implementation outcomes show that SVM is better than ANN regarding response time and Acc.