Full Program
Summary:
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 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 5G 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 Flood and RequestMessages for Deregistration Flooding are highly effective in distinguishing between benign and malicious traffic. Furthermore, SHAP-based feature importance analysis validates the dataset’s applicability for AI-driven anomaly detection. By bridging this gap, our dataset enables researchers to advance 5G security mechanisms and optimize resource management strategies.Author(s):
Beny Nugraha
Chemnitz University of Technology
Germany
Mehrdad Hajizadeh
Chemnitz University of Technology
Germany
Tim Niehoff
ipoque GmbH - A Rohde & Schwarz Company
Germany
Abhishek Venkatesh Jnanashree
Chemnitz University of Technology
Germany
Trung V. Phan
Chemnitz University of Technology
Germany
Dionysia Triantafyllopoulou
Chemnitz University of Technology
Germany
Oliver Krause
ipoque GmbH - A Rohde & Schwarz Company
Germany
Martin Mieth
ipoque GmbH - A Rohde & Schwarz Company
Germany
Klaus Moessner
Chemnitz University of Technology
Germany
Thomas Bauschert
Chemnitz University of Technology
Germany