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Award ID contains: 2132281

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  1. ; ; ; ; ; ( , Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)
    Prior work has developed numerous systems that test the security and safety of smart homes. For these systems to be applicable in practice, it is necessary to test them with realistic scenarios that represent the use of the smart home, i.e., home automation, in the wild. This demo paper presents the technical details and usage of Helion, a system that uses n-gram language modeling to learn the regularities in user-driven programs, i.e., routines developed for the smart home, and predicts natural scenarios of home automation, i.e., event sequences that reflect realistic home automation usage. We demonstrate the HelionHA platform, developed by integrating Helion with the popular Home Assistant smart home platform. HelionHA allows an end-to-end exploration of Helion’s scenarios by executing them as test cases with real and virtual smart home devices. 
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    Free, publicly-accessible full text available December 6, 2024
  2. ; ; ; ; ( , Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security)
    Full Text Available 
  3. ; ; ; ; ( , Proceedings of the 31st USENIX Security Symposium)
    Accepted Manuscript Full Text Available