This dataset presents real-world IoT device traffic captured under a scenario termed "Active," reflecting typical usage patterns encountered by everyday users. Our methodology emphasizes the collection of authentic data, employing rigorous testing and system evaluations to ensure fidelity to real-world conditions while minimizing noise and irrelevant capture.
This dataset results from a month-long cloud-based Internet Background Radiation observation conducted in May 2023.
A sensor fleet comprised of 26 EC2 compute instances was deployed within Amazon Web Services across their 26 commercially available regions, 1 sensor per region.
In this dataset, we provide detailed traffic stream data for the Spot robot, including both the Spot robot control traffic stream and the Spot video stream. The Spot robot traffic streams provide realistic traffic data for communication network evaluations, e.g., for measurements with the TSN FlexText testbed. Furthermore, we share data for the tactile internet including audio, video, and robotic communication. Finally, the dataset includes generic data streams for three different intervals (0.2ms, 0.3ms, and 0.5ms) with two different Ethernet frame sizes.
iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network.
The dataset is generated by performing different Man-in-the-Middle (MiTM) attacks in the synthetic cyber-physical electric grid in RESLab Testbed at Texas AM University, US. The testbed consists of a real-time power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master, SEL real-time automation controller (RTAC), and Cisco Layer-3 switch. With different scenarios of MiTM attack, we implement a logic-based defense mechanism in RTAC and save the traffic data and related cyber alert data under the attack.