Microcontroller-Based Multi-Sensor IoT Testbed for Cyberattack Simulation, Data Creation, and Intrusion Detection

The Internet of Things (IoT) continues to expand rapidly, bringing with it a new set of security concerns. Many IoT devices are lightweight and limited in processing power, which makes them vulnerable when used in critical applications. Although there are several datasets available for network-level intrusion detection, very few provide practical guidance on how microcontrollers and physical sensors can be combined to recreate realistic attack scenarios in a way that supports reproducible research. In this work, we present a microcontroller-based testbed that integrates multiple sensors to capture, store, and analyze data under both normal operation and simulated cyberattacks. The testbed connects environmental, motion, and network sensors to model IoT systems that are exposed to common threats including Denial-of-Service (DoS), Distributed DoS (DDoS), and Slowloris attacks. The system enables direct sensor-to-microcontroller connections, real-time logging, and synchronized event monitoring, allowing for detailed analysis of how devices behave under attack. Environmental and physical sensor measurements are included to provide synchronized cyber-physical context and baseline operational information rather than demonstrated direct indicators of the investigated network attacks. Alongside the testbed, we share a dataset containing sensor readings, system metrics, and labeled attack events. This contribution provides a practical resource for researchers and educators interested in IoT security, machine-learning-based intrusion detection, and the development of countermeasures rooted in real-world data. By combining low-level hardware experimentation with higher-level security analysis, this work creates a foundation for advancing experimental approaches to cybersecurity in IoT environments.