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.

在当今网络安全领域中，组织机构对强大的攻击检测机制的需求日益凸显。然而，当前软件定义网络（SDN）研究在基于SDN-OpenFlow的最近数据集方面存在显著的不足。本研究旨在填补这一空白，通过引入一个针对软件定义网络（SDN）入侵检测的新型数据集。该数据集源自于从真实流量中收集的OpenFlow统计数据，整合了广泛的网络活动。在当今网络安全领域中，对于强大的攻击检测机制的重要性不言而喻。本研究通过利用多种机器学习和深度学习算法进行的实证评估，该数据集对于评估SDN环境中的入侵检测系统以及深化对软件定义网络流量模式的理解具有重要价值。