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

• DOI: 10.1016/j.procs.2023.09.007
• Publication Date: 2023-10-10T19:49:49Z
• Authors: Lamiae Boukraa; Siham Essahraui; Khalid El Makkaoui; Ibrahim Ouahbi; Redouane Esbai

Software-defined networking (SDN) has emerged as a promising approach for managing network infrastructure through 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 mitigate these This study compares two ML models, namely support vector machines (SVM) artificial neural networks (ANN), intelligent intrusion in SDN. To assess performance we utilized NSL-KDD dataset, with specific emphasis attacks, compared their accuracy (Acc), precision, recall, F1-score metrics. The implementation outcomes show that SVM is better than ANN regarding response time Acc.