The convergence of the Internet of Things (IoT) and Software-Defined Networking (SDN) has enabled scalability, dynamic resource allocation, and centralized management of SDN-IoT infrastructures. However, the integration introduces critical security challenges such as cross-layer threats and IoT-specific exploits. While Machine Learning (ML)–based Intrusion Detection Systems (IDS) offer promising defense, their effectiveness is hindered by inadequate and outdated datasets that lack realistic SDN control-plane dynamics and IoT heterogeneity within integrated SDN-IoT environments. Critically, no existing dataset simultaneously captures three dimensions, including SDN control-plane behaviour, IoT device traffic, and cross-layer attack vectors essential for securing modern converged networks. To address the gap, we propose a novel dataset generated from a real-world SDN-IoT testbed. The dataset is validated using five ML models with two classification schemes. The experimental results show high detection performance, with 100% accuracy across attack categories and 88.5% accuracy across individual attack types.