SoK: Context Sensing for Access Control in the Adversarial Home IoT

He, Weiji; Zhao, Valerie; Morkved, Olivia; Siddiqui, Sabeeka; Fernandes, Earlence; Hester, Josiah; Ur, Blase

doi:10.1109/eurosp51992.2021.00014

Cited by 27 publications

(35 citation statements)

References 102 publications

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“…Additionally, there have been efforts on forensics analysis [67,68] and risk-based analysis [2, 18,56], designing access control [29,33,42], repairing or synthesizing automation rules [75], addressing information leakage and privacy [11,24,62], inferring device behaviors for anomaly detection using machine learning-based network traffic/flow analysis [1,9,27,31,46,50,52,54,63,76] and fuzzing smartphone companion apps for IoT [14,59]. These efforts are orthogonal to the safety problem that MAVERICK addresses.…”

Section: Related Workmentioning

confidence: 99%

MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime

Mazhar¹,

Li²,

Hoque³

et al. 2023

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%

MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime

Mazhar¹,

Li²,

Hoque³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…He et al [106] surveyed academic literature on context sensing for access control in smart homes, and pointed out flaws in existing systems under adversarial attacks. Context-sensing is just one of the approaches adopted by the systems we reviewed in this SoK paper.…”

Section: Related Workmentioning

confidence: 99%

SoK: Privacy-enhancing Smart Home Hubs

Zavalyshyn

Legay

Rath

et al. 2022

PoPETs

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Smart homes are IoT systems enabling the automation of household operation. The unrestricted collection and processing of data by smart home systems raises legitimate privacy concerns for their users. Over the past decade, there has been significant interest in privacy-enhancing technologies applied at the level of a local smart hub physically located in the home and acting as a gateway between sensors, applications, platform providers, and services in the cloud. The number and variety of projects and research proposals can, however, make their comparison a daunting and unnecessarily complex task. We systematize existing knowledge in this field through the analysis and categorization of 10 industrial and community-contributed systems and 37 research proposals from the literature of the past 11 years. Our results shed light on the diversity of system and trust models considered in the state-of-the-art and on the associated privacy-enhancing technologies. We further identify open research problems and promising approaches that would benefit the smart home hub model and the protection of smart home users’ privacy.

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“…IoTSafe [12] further combines static analysis with dynamic testing to tailor the discovery of physical side-channels to the current deployment. A recent survey by He et al [22] gives a good overview on how context sensing can be used for access control in these systems. While these systems help to reduce the risk that physical channel interactions pose, they do not protect against cases where an existing and desired trigger-action rule is being exploited, e.g., by a compromised smart home device.…”

Section: Security Of Smart Home Systemsmentioning

confidence: 99%

Haunted House: Physical Smart Home Event Verification in the Presence of Compromised Sensors

Birnbach

Eberz

Martinovic

2022

ACM Trans. Internet Things

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In this article, we verify physical events using data from an ensemble of smart home sensors. This approach both protects against event sensor faults and sophisticated attackers. To validate our system’s performance, we set up a “smart home” in an office environment. We recognize 22 event types using 48 sensors over the course of two weeks. Using data from the physical sensors, we verify the event stream supplied by the event sensors to detect both masking and spoofing attacks. We consider three threat models: a zero-effort attacker, an opportunistic attacker, and a sensor-compromise attacker who can arbitrarily modify live sensor data. For spoofed events, we achieve perfect classification for 9 out of 22 events and achieve a 0% false alarm rate at a detection rate exceeding 99.9% for 15 events. For 11 events the majority of masking attacks can be detected without causing any false alarms. We also show that even a strong opportunistic attacker is inherently limited to spoofing few select events and that doing so involves lengthy waiting periods. Finally, we demonstrate the vulnerability of a single-classifier system to compromised sensor data and introduce a more secure approach based on sensor fusion.

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SoK: Context Sensing for Access Control in the Adversarial Home IoT

Cited by 27 publications

References 102 publications

MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime

MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime

SoK: Privacy-enhancing Smart Home Hubs

Haunted House: Physical Smart Home Event Verification in the Presence of Compromised Sensors

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