Sunil Manandhar

Designing practical security systems for the smart home is challenging without the knowledge of realistic home usage. We describe the design and implementation of Helion, a framework that generates natural home automation scenarios by identifying the regularities in user-driven home automation sequences, which are in turn generated from routines created by end-users. Our key hypothesis is that smart home event sequences created by users exhibit inherent semantic patterns, or naturalness that can be modeled and used to generate valid and useful scenarios. To evaluate our approach, we first empirically demonstrate that this naturalness hypothesis holds, with a corpus of 30,518 home automation events, constructed from 273 routines collected from 40 users. We then demonstrate that the scenarios generated by Helion seem valid to end-users, through two studies with 16 external evaluators. We further demonstrate the usefulness of Heli-on’s scenarios by addressing the challenge of policy specification, and using Helion to generate 17 security/safety policies with minimal effort. We distill 16 key findings from our results that demonstrate the strengths of our approach, surprising aspects of home automation, as well as challenges and opportunities in this rapidly growing domain.

Sunil Manandhar is a fourth-year Ph.D. candidate in the Computer Science Department at William & Mary. His research areas include Internet of Things (IoT) security, privacy, and application security. He is currently exploring how Natural Language Processing (NLP) techniques can be used to improve security and privacy in emerging IoT platforms. He holds Bachelors in Computer Science and Information Technology (B.Sc.CSIT) from Tribhuwan University, Nepal.