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As the smart home IoT ecosystem flourishes, it is imperative to gain a better understanding of the unique challenges it poses in terms of management, security, and privacy. Prior studies are limited because they examine smart home IoT devices in testbed environments or at a small scale. To address this gap, we present a measurement study of smart home IoT devices in the wild by instrumenting home gateways and passively collecting real-world network traffic logs from more than 200 homes across a large metropolitan area in the United States. We characterize smart home IoT traffic in terms of its volume, temporal patterns, and external endpoints along with focusing on certain security and privacy concerns. We first show that traffic characteristics reflect the functionality of smart home IoT devices such as smart TVs generating high volume traffic to content streaming services following diurnal patterns associated with human activity. While the smart home IoT ecosystem seems fragmented, our analysis reveals that it is mostly centralized due to its reliance on a few popular cloud and DNS services. Our findings also highlight several interesting security and privacy concerns in smart home IoT ecosystem such as the need to improve policy-based access control for IoT traffic, lack of use of application layer encryption, and prevalence of third-party advertising and tracking services. Our findings have important implications for future research on improving management, security, and privacy of the smart home IoT ecosystem.

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Safety and security issues in programmable IoT systems are still a pressing problem. Many solutions have been proposed to curb unexpected behavior of automation apps installed on IoT platforms by enforcing safety and security policies at runtime. However, all prior work addresses a weaker version of the actual problem as they consider a simple threat model, which is far from the reality. Moreover, these solutions are heavily dependent on the installed apps and catered to specific IoT platforms, which can unfortunately result in inaccurate runtime enforcement of policies. In this paper, we address a stronger version of the problem by considering a realistic threat model, where (i) undesired cyber actions (e.g., lock()/unlock()) can come from not only automation platform backends (e.g., SmartThings) but also close-sourced thirdparty services (e.g., IFTTT), and (ii) physical actions (e.g., user interactions) on devices can move the IoT system to an unsafe state. We propose a runtime mechanism, dubbed Maverick, which employs an app-independent, platform-agnostic mediator to enforce policies against all undesired cyber actions and applies corrective-actions to bring the IoT system back to a safe state if it ever transitions to an unsafe state. To assist users for writing policies, Maverick is equipped with a policy language capable of expressing rich temporal invariants and an automated toolchain that includes a policy synthesizer and a policy analyzer. We implemented Maverick in a prototype and showed its efficacy in both physical and virtual testbeds where it incurred minimal overhead.