Sybil attacks pose a significant threat to the integrity and reliability of Internet of Things (IoT) networks. In these attacks, a single malicious actor fraudulently adopts multiple identities to disrupt network functionality, compromise data accuracy, and manipulate routing or decision-making processes. Mitigating Sybil attacks in IoT systems demands effective detection techniques and adaptive security measures. This paper introduces a novel dataset designed specifically to detect Sybil attacks targeting IoT devices within Software-Defined Networking (SDN) environments. The dataset was created using a Mininet-based testbed, incorporating a Ryu controller and OpenFlow enabled switches to emulate realistic IoT network conditions. It captures a wide spectrum of traffic behaviors, including normal operations and various Sybil attack scenarios, with features such as identity patterns, flow dynamics, packet exchange traits, and protocol specific indicators. The dataset is intended to support the development of machine learning models capable of accurately identifying Sybil attack signatures in SDN-enabled IoT networks. Initial results highlight the dataset’s potential to improve detection accuracy and responsiveness, contributing to more secure and robust IoT infrastructures.