A Comprehensive 5G Dataset for Control and Data Plane Security and Resource Management

Ensuring the security and resilience of 5G networks requires comprehensive datasets that capture both control and data plane traffic. However, publicly available datasets remain limited, particularly those covering real-world attack scenarios and resource allocation. To address this gap, we introduce a dataset that includes diverse attack vectors targeting both planes, such as flooding, fuzzing, and Packet Forwarding Control Protocol (PFCP)-based Denial-of-Service (DoS) attacks. The dataset is collected from open-source and commercial testbeds, as well as a MATLAB-based simulation of 5 G resource allocation patterns. We provide statistical and correlation analyses to highlight key attack indicators, demonstrating that features such as src2dst_mean_piat_ms for ICMP floods and RequestMessages for Deregistration floods are highly effective in distinguishing between benign and malicious traffic. Furthermore, SHAP-based feature importance analysis validates the dataset’s applicability for Artificial Intelligence (AI)-driven anomaly detection. By bridging this gap, our dataset enables researchers to advance 5G security mechanisms and optimize resource management strategies.