IoTFlow: Inferring IoT Device Behavior at Scale through Static Mobile Companion App Analysis

Abstract

The number of “smart” devices, that is, devices making up the Internet of Things (IoT), is steadily growing. They suffer from vulnerabilities just as other software and hardware. Automated analysis techniques can detect and address weaknesses before attackers can misuse them. Applying existing techniques or developing new approaches that are sufficiently general is challenging though. Contrary to other platforms, the IoT ecosystem features various software and hardware architectures. We introduce IoTFlow, a new static analysis approach for IoT devices that leverages their mobile companion apps to address the diversity and scalability challenges. IoTFlow combines Value Set Analysis (VSA) with more general data-flow analysis to automatically reconstruct and derive how companion apps communicate with IoT devices and remote cloud-based backends, what data they receive or send, and with whom they share it. To foster future work and reproducibility, our IoTFlow implementation is open source. We analyze 9,889 manually verified companion apps with IoTFlow to understand and characterize the current state of security and privacy in the IoT ecosystem, which also demonstrates the utility of IoTFlow. We compare how these IoT apps differ from 947 popular general-purpose apps in their local network commu- nication, the protocols they use, and who they communicate with. Moreover, we investigate how the results of IoTFlow compare to dynamic analysis, with manual and automated interaction, of 13 IoT devices when paired and used with their companion apps. Overall, utilizing IoTFlow, we discover various IoT security and privacy issues, such as abandoned domains, hard-coded credentials, expired certificates, and sensitive personal information being shared.