SDNFlow Dataset

In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-based datasets. This study seeks to bridge this gap by introducing a novel dataset for intrusion detection in Software-Defined Networking (SDN). The dataset, derived from OpenFlow statistics gathered from real traffic, integrates a comprehensive range of network activities.In the contemporary cybersecurity landscape, robust attack detection mechanisms are importanttivities. An empirical evaluation leveraging diverse Machine and deep Learning algorithms was performed. The dataset is valuable for evaluating intrusion detection systems withinSDN environments and deepening the understanding of traffic patterns in Software Defined Networks.