Toward Secure SDN Infrastructure in Smart Cities: Kafka-Enabled Machine Learning Framework for Anomaly Detection

Gayathri Karthick, Glenford Mapp, Jon Crowcroft

As smart cities evolve, the demand for real-time, secure, and adaptive network monitoring, continues to grow. Software-Defined Networking (SDN) offers a centralized approach to managing network flows; However, anomaly detection within SDN environments remains a significant challenge, particularly at the intelligent edge. This paper presents a conceptual Kafka-enabled ML framework for scalable, real-time analytics in SDN environments, supported by offline evaluation and a prototype streaming demonstration. A range of supervised ML models covering traditional methods and ensemble approaches (Random Forest, Linear Regression & XGBoost) were trained and validated using the InSDN intrusion detection dataset. These models were tested against multiple cyber threats, including botnets, dos, ddos, network reconnaissance, brute force, and web attacks, achieving up to 99% accuracy for ensemble classifiers under offline conditions. A Dockerized prototype demonstrates Kafka’s role in offline data ingestion, processing, and visualization through PostgreSQL and Grafana. While full ML pipeline integration into Kafka remains part of future work, the proposed architecture establishes a foundation for secure and intelligent Software-Defined Vehicular Networking (SDVN) infrastructure in smart cities.