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Consumer Internet of things research often involves collecting networktraffic sent or received by IoT devices. These data are typically collected viacrowdsourcing or while researchers manually interact with IoT devices in alaboratory setting. However, manual interactions and crowdsourcing are oftentedious, expensive, inaccurate, or do not provide comprehensive coverage ofpossible IoT device behaviors. We present a new method for generating IoTnetwork traffic using a robotic arm to automate user interactions with devices.This eliminates manual button pressing and enables permutation-basedinteraction sequences that rigorously explore the range of possible devicebehaviors. We test this approach with an Arduino-controlled robotic arm, asmart speaker, and a smart thermostat, using machine learning to demonstratethat collected network traffic contains information about device interactionsthat could be useful for network, security, or privacy analyses. We alsoprovide source code and documentation allowing researchers to easily automateIoT device interactions and network traffic collection in future studies.

Automating Internet of Things Network Traffic Collection with Robotic Arm Interactions