Automating Internet of Things Network Traffic Collection with Robotic Arm Interactions

TL;DR

This paper introduces a robotic arm-based method to automate IoT device interactions for comprehensive network traffic collection, improving accuracy and coverage over manual or crowdsourced approaches.

Contribution

The authors present a novel robotic automation technique for IoT traffic data collection, enabling permutation-based interaction sequences and detailed behavior exploration.

Findings

Collected traffic contains identifiable device interaction information.

Automation improves coverage and reduces manual effort.

Method applicable to various IoT devices and research contexts.

Abstract

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