Kevin Moran, Assistant Professor, Computer Science, is set to receive funding from the National Science Foundation for a project that aims to provide researchers, end-users, and system designers with the means to collect, generate, and analyze realistic examples of home automation usage.
This project is centered on three specific goals.
First, it will develop novel data collection strategies that allow end-users to easily specify routines in a flexible manner, as well as techniques based on Natural Language Processing (NLP) for automatically processing and transforming the data into a format suitable for modeling.
Second, it will introduce approaches for transforming routines into realistic home automation event sequences, understanding their latent properties and modeling them using well-understood language modeling techniques.
Third, it will contextualize the smart home usage models to make predictions that cater to security analyses specifically and develop tools that allow for the inspection of a smart home’s state alongside the execution of predicted event sequences on real products.
The techniques and models developed during the course of this project will be validated with industry partners and are expected to become instrumental for developers and researchers to understand security and privacy properties of smart homes.
Regarding the importance of this research, Moran said, “User-customizable smart-homes are becoming increasingly popular with the number of internet-connected home devices on the market. Given the personal nature of many of these devices, researchers have been working to ensure that smart home data remains private and secure. However, it has been difficult to understand how consumers actually use smart home products in connection with one another, which can greatly impact security research. In this research project, we aim to provide researchers with techniques that will allow them to measure smart home security under realistic usage conditions.”
Moran will receive $382,423 from NSF for this project. Funding will begin in January 2022 and will end in late December 2024.
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