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.
翻译:物品的消费者互联网研究往往涉及收集由IoT装置发送或接收的网络流量。这些数据通常是通过众包收集的,或研究人员在实验室环境中与IoT装置人工互动时收集的。然而,人工互动和众包往往乏味、昂贵、不准确,或者没有全面覆盖可能的IoT装置行为。我们提出了一个新方法,利用机器人臂生成IoT网络流量,将用户与装置的互动自动化。这消除了手动按键,并使得基于变异的互动序列能够严格探索可能的装置行为范围。我们用Arduino控制的机器人臂、智能扬声器和智能自动调温器测试这一方法,利用机器学习来证明所收集的网络流量包含设备互动的信息,可用于网络、安全或隐私分析。我们还提供源代码和文件,使研究人员能够方便地自动连接IoT装置的相互作用和网络流量收集在未来的研究中进行。