An official website of the United States government
The vision of smart homes is rapidly becoming a reality, as the Internet of Things and other smart devices are deployed widely. Although smart devices offer convenience, they also create a significant management problem for home residents. With a large number and variety of devices in the home, residents may find it difficult to monitor, or even locate, devices. A central controller that brings all the home’s smart devices under secure management and a unified interface would help homeowners and residents track and manage their devices. We envision a solution called the SPLICEcube whose goal is to detect smart devices, locate them in three dimensions within the home, securely monitor their network traffic, and keep an inventory of devices and important device information throughout the device’s lifecycle. The SPLICEcube system consists of the following components: 1) a main cube, which is a centralized hub that incorporates and expands on the functionality of the home router, 2) a database that holds network data, and 3) a set of support cubelets that can be used to extend the range of the network and assist in gathering network data. To deliver this vision of identifying, securing, and managing smart devices, we introduce an architecture that facilitates intelligent research applications (such as network anomaly detection, intrusion detection, device localization, and device firmware updates) to be integrated into the SPLICEcube. In this thesis, we design a general-purpose Wi-Fi architecture that underpins the SPLICEcube. The architecture specifically showcases the functionality of the cubelets (Wi-Fi frame detection, Wi-Fi frame parsing, and transmission to cube), the functionality of the cube (routing, reception from cubelets, information storage, data disposal, and research application integration), and the functionality of the database (network data storage). We build and evaluate a prototype implementation to demonstrate our approach is scalable to accommodate new devices and extensible to support different applications. Specifically, we demonstrate a successful proof-of-concept use of the SPLICEcube architecture by integrating a security research application: an "Inside-Outside detection" system that classifies an observed Wi-Fi device as being inside or outside the home.
more »
« less
This content will become publicly available on November 21, 2025
Moat: Adaptive Inside/Outside Detection System for Smart Homes
Smart-home technology is now pervasive, demanding increased attention to the security of the devices and the privacy of the home's residents. To assist residents in making security and privacy decisions - e.g., whether to allow a new device to connect to the network, or whether to be alarmed when an unknown device is discovered - it helps to know whether the device is inside the home, or outside. In this paper we present MOAT, a system that leverages Wi-Fi sniffers to analyze the physical properties of a device's wireless transmissions to infer whether that device is located inside or outside of a home. MOAT can adaptively self-update to accommodate changes in the home indoor environment to ensure robust long-term performance. Notably, MOAT does not require prior knowledge of the home's layout or cooperation from target devices, and is easy to install and configure. We evaluated MOAT in four different homes with 21 diverse commercial smart devices and achieved an overall balanced accuracy rate of up to 95.6%. Our novel periodic adaptation technique allowed our approach to maintain high accuracy even after rearranging furniture in the home. MOAT is a practical and efficient first step for monitoring and managing devices in a smart home.
more »
« less
- Award ID(s):
- 1955805
- PAR ID:
- 10618322
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 8
- Issue:
- 4
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 31
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In the smart home landscape, there is an increasing trend of homeowners sharing device access outside their homes. This practice presents unique challenges in terms of security and privacy. In this study, we evaluated the co-management features in smart home management systems to investigate 1) how homeowners establish and authenticate shared users’ access, 2) the access control mechanisms, and 3) the management, monitoring, and revocation of access for shared devices. We conducted a systematic feature analysis of 11 Android and iOS mobile applications (“apps”) and 2 open-source platforms designed for smart home management. Our study revealed that most smart home systems adopt a centralized control model which necessitates shared users to utilize the primary app for device access, while providing diverse sharing mechanisms, such as email or phone invitations and unique codes, each presenting distinct security and privacy advantages. Moreover, we discovered a variety of access control options, ranging from full access to granular access control such as time-based restrictions which, while enhancing security and convenience, necessitate careful management to avoid user confusion. Additionally, our findings highlighted the prevalence of comprehensive methods for monitoring shared users’ access, with most systems providing detailed logs for added transparency and security, although there are some restrictions to safeguard homeowner privacy. Based on our findings, we recommend enhanced access control features to improve user experience in shared settings.more » « less
-
Smart home IoT devices are becoming increasingly popular. Modern programmable smart home hubs such as SmartThings enable homeowners to manage devices in sophisticated ways to save energy, improve security, and provide conveniences. Unfortunately, many smart home systems contain vulnerabilities, potentially impacting home security and privacy. This paper presents Vigilia, a system that shrinks the attack surface of smart home IoT systems by restricting the network access of devices. As existing smart home systems are closed, we have created an open implementation of a similar programming and configuration model in Vigilia and extended the execution environment to maximally restrict communications by instantiating device-based network permissions. We have implemented and compared Vigilia with forefront IoT-defense systems; our results demonstrate that Vigilia outperforms these systems and incurs negligible overhead.more » « less
-
The increased adoption of smart home cameras (SHCs) foregrounds issues of surveillance, power, and privacy in homes and neighborhoods. However, questions remain about how people are currently using these devices to monitor and surveil, what the benefits and limitations are for users, and what privacy and security tensions arise between primary users and other stakeholders. We present an empirical study with 14 SHC users to understand how these devices are used and integrated within everyday life. Based on semistructured qualitative interviews, we investigate users’ motivations, practices, privacy concerns, and social negotiations. Our findings highlight the SHC as a perceptually powerful and spatially sensitive device that enables a variety of surveillant uses outside of basic home security—from formally surveilling domestic workers, to casually spying on neighbors, to capturing memories. We categorize surveillant SHC uses, clarify distinctions between primary and non primary users, and highlight under-considered design directions for addressing power imbalances among primary and non-primary users.more » « less
-
With the availability of Internet of Things (IoT) devices offering varied services, smart home environments have seen widespread adoption in the last two decades. Protecting privacy in these environments becomes an important problem because IoT devices may collect information about the home’s occupants without their knowledge or consent. Furthermore, a large number of devices in the home, each collecting small amounts of data, may, in aggregate, reveal non-obvious attributes about the home occupants. A first step towards addressing privacy is discovering what devices are present in the home. In this paper, we formally define device discovery in smart homes and identify the features that constitute discovery in that environment. Then, we propose an evaluative rubric that rates smart home technology initiatives on their device discovery capabilities and use it to evaluate four commonly deployed technologies. We find none cover all device discovery aspects. We conclude by proposing a combined technology solution that provides comprehensive device discovery tailored to smart homes.more » « less
