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Title: A Study of Data Store-based Home Automation
Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we perform a systematic security evaluation of two popular smart home platforms, Google's Nest platform and Philips Hue, that implement home automation "routines" (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store. Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines. This analysis results in ten key findings with serious security implications. For instance, we demonstrate the potential for the misuse of smart home routines in the Nest platform to perform a lateral privilege escalation, illustrate how Nest's product review system is ineffective at preventing multiple stages of this attack that it examines, and demonstrate how emerging platforms may fail to provide even bare-minimum security by allowing apps to arbitrarily add/remove other apps from the user's smart home. Our findings draw attention to the unique security more » challenges of platforms that execute routines via centralized data stores, and highlight the importance of enforcing security by design in emerging home automation platforms. « less
Authors:
; ; ; ;
Award ID(s):
1815336
Publication Date:
NSF-PAR ID:
10173156
Journal Name:
Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy
Page Range or eLocation-ID:
73 to 84
Sponsoring Org:
National Science Foundation
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