Paper accepted at IMC ’23.
BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models
Tianrui Hu (Northeastern University), Daniel J. Dubois (Northeastern University), David Choffnes (Northeastern University)
Last updated: 10/24/2023
News
- 09/23/2023. This research has been accepted for publication at the 23rd ACM Internet Measurement Conference (IMC 2023) with the paper titled “BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models.“
Abstract
Smart home IoT platforms are typically closed systems, meaning that there is poor visibility into device behavior. Understanding device behavior is important not only for determining whether devices are functioning as expected, but also can reveal implications for privacy (e.g., surreptitious audio/video recording), security (e.g., device compromise), and safety (e.g., denial of service on a baby monitor). While there has been some work on identifying devices and a handful of activities, an open question is what is the extent to which we can automatically model the entire behavior of an IoT deployment, and how it changes over time, without any privileged access to IoT devices or platform messages.
In this work, we demonstrate that the vast majority of IoT behavior can indeed be modeled, using a novel multi-dimensional approach that relies only on the (often encrypted) network traffic exchanged by IoT devices. Our key insight is that IoT behavior (including cross-device interactions) can often be captured using relatively simple models such as timers (for periodic behavior) and probabilistic state-machines (for user-initiated behavior and devices interactions) during a limited observation phase. We then propose deviation metrics that can identify when the behavior of an IoT device or an IoT system changes over time. Our models and metrics successfully identify several notable changes in our IoT deployment, including a camera that changed locations, network outages that impact connectivity, and device malfunctions.
About this publication
Paper title: BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models
Authors: Tianrui Hu (Northeastern University), Daniel J. Dubois (Northeastern University), David Choffnes (Northeastern University)
Full Text: available via ACM open access.
Software: available on Github.
Dataset: available here.
Presentation: slides
Citation:
@inproceedings{hu-imc23,
title={{BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models}},
author={Hu, Tianrui and Dubois, Daniel J. and Choffnes, David},
booktitle={Proc. of the Internet Measurement Conference (IMC'23)},
year={2023}
}
Acknowledgments
This research was partially supported by the NSF (BehavIoT CNS-1909020, ProperData SaTC-1955227).