Collaborative Research: IMR: MM-1C: Privacy-preserving IoT Analytics and Behavior Prediction on Network Edge

As the Internet of Things (IoT) products gain popularity on home networks, they disproportionately overburden home networks from a resource, security, and privacy point of view. Despite recent efforts to characterize IoT traffic, there is a lack of annotated datasets. Moreover, existing annotated traffic typically covers a handful of devices in a lab setting.

Therefore, it is unclear if the results generalize to a large-scale real-world setting. Similarly, when it comes to developing privacy-preserving IoT analytics, existing approaches rely on simulated data or datasets that are not representative of real-world IoT traffic. To this end, this project explores new Internet measurement methodologies and privacy-preserving analytics for IoT devices.

The project's broader significance and importance are to help network providers and consumers better manage their networks in terms of quality of service, security and privacy.

This project first focuses on developing a new infrastructure to automatically collect and annotate IoT traffic by programmatically triggering different functionalities on IoT devices via companion apps and hidden APIs. To complement the annotation process, the project enables real-world participants to annotate the network traffic generated by their smart home IoT devices.

Next, the project builds privacy-preserving machine learning models to predict traffic volume, traffic type, and anomalous device behavior even in the presence of imbalanced training data across the different tasks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.