Performance measurement dataset for open RAN with user mobility and security threats

We present a comprehensive dataset collected from an Open-RAN (O-RAN) deployment in our OpenIreland testbed, aimed at facilitating advanced research in Radio Access Network (RAN). The dataset includes RAN measurements from users engaged in diverse traffic classes such as Web Browsing, Voice over IP (VoIP), Internet of Things (IoT), and Video Streaming, as well as malignant traffic classes including DDoS Ripper, DoS Hulk, and Slow Loris attacks. These measurements encompass various mobility patterns, including Static, Pedestrian, Train, Car, and Bus users. While Wi-Fi datasets, including probe requests, Wi-Fi fingerprints, and signal strengths, are common in the literature, and mobile networks present abundant research opportunities with billions of global subscribers, datasets with RAN Key Performance Indicator (KPI) measurements are relatively rare. This scarcity is particularly notable in the context of O-RAN networks, which have been scrutinized for higher potential vulnerability compared to single-vendor solutions. Our work addresses this gap by collecting and publicly sharing a dataset rich in RAN KPIs from our O-RAN deployment. We utilized this dataset to classify different traffic classes for the detection of service-level attacks. Beyond its immediate use for attack detection, the dataset is versatile, supporting research in intrusion detection, network protection strategies, and numerous other RAN-related challenges. By offering extensive performance metrics, this dataset enables researchers to explore issues like power consumption, Channel Quality Indicator (CQI)/Modulation and Coding Scheme (MCS) optimization, resource management, cell characterization, and more. We believe that this dataset will significantly advance the development of robust, efficient, and secure RAN solutions.