Systematically Ensuring the Confidence of Real-Time Home Automation IoT Systems

Bu, Lei; Xiong, Wenhui; Liang, Chieh-Jan Mike; Han, Soohee; Zhang, Dongmei; Lin, Shan; Li, Xuandong

doi:10.1145/3185501

Cited by 30 publications

(19 citation statements)

References 22 publications

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“…[11] Peeves [7] Helion [28] iRuler [43] IoTMon [18] Menshen [8] HomeGuard [15] iRuler [43] uses SMT and model checking to detect vulnerabilities among IFTTT rules, especially in rule configurations of IoT deployments. Menshen [8] builds and checks the Linear Hybrid Automata (LHA) model for user rules to find violations and generate fix suggestions for non-expert users. Home-Guard [15] uses SMT models to check rules and configurations to find cross-app interference threats among smart home apps.…”

Section: Related Workmentioning

confidence: 99%

IoTSafe: Enforcing Safety and Security Policy with Real IoT Physical Interaction Discovery

Ding

Cheng

2021

Proceedings 2021 Network and Distributed System Security Symposium

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The Internet of Things (IoT) platforms bring significant convenience for increased home automation. Especially, these platforms provide many new features for managing multiple IoT devices to control their physical surroundings. However, these features also bring new safety and security challenges. For example, an attacker can manipulate IoT devices to launch attacks through unexpected physical interactions. Unfortunately, very few existing research investigates the physical interactions among IoT devices and their impacts on IoT safety and security. In this paper, we propose a novel dynamic safety and security policy enforcement system called IOTSAFE, which can capture and manage real physical interactions considering contextual features on smart home platforms. To identify real physical interactions of IoT devices, we present a runtime physical interaction discovery approach, which employs both static analysis and dynamic testing techniques to identify runtime physical interactions among IoT devices. In addition, IOTSAFE constructs physical models for temporal physical interactions, which can predict incoming risky situations and block unsafe device states accordingly. We implement a prototype of IOTSAFE on the SmartThings platform. Our extensive evaluations demonstrate that IOTSAFE effectively identifies 39 real physical interactions among 130 potential interactions in our experimental environment. IOTSAFE also successfully predicts risky situations related to temporal physical interactions with nearly 96% accuracy and prevents highly risky conditions.

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Section: Related Workmentioning

confidence: 99%

IoTSafe: Enforcing Safety and Security Policy with Real IoT Physical Interaction Discovery

Ding

Cheng

2021

Proceedings 2021 Network and Distributed System Security Symposium

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“…For example, it deines the attributes and commands of a device type, the efects on environmental variables (e.g., increasing temperature) of a command, and state transitions of a device command (i.e., what events will be generated by the execution of a command). Device/Service metadata can be constructed by analyzing the documentation of an IoT platform or provided by the platform developers or experts [28,51]. For the IFTTT platform, we construct the service metadata by crawling the web page of each service to get what triggers and actions the service supports.…”

Section: Formal Modeling With Model Buildermentioning

confidence: 99%

“…7 Conversely, these works also provide several useful properties that we did not consider in iRuler. AutoTap [90] presents a method for verifying coniguration properties as expressed by novice users, and joins MenShen [28], Salus [51], and SIFT [52] in supporting automated creation and repair of rules (Rule Writing). Systems like Soteria [30], IoTSan [70], and Home-Guard (arXiv preprint only: [32]) are based on source code analysis of IoT apps and can therefore consider additional factors such as iner-grained reduction of state explosion and speciic malicious input sequences.…”

Section: Related Workmentioning

confidence: 99%

Charting the Attack Surface of Trigger-Action IoT Platforms

Wang

Datta

Yang³

et al. 2019

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

122

108

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Internet of Things (IoT) deployments are becoming increasingly automated and vastly more complex. Facilitated by programming abstractions such as trigger-action rules, end-users can now easily create new functionalities by interconnecting their devices and other online services. However, when multiple rules are simultaneously enabled, complex system behaviors arise that are diicult to understand or diagnose. While history tells us that such conditions are ripe for exploitation, at present the security states of trigger-action IoT deployments are largely unknown.In this work, we conduct a comprehensive analysis of the interactions between trigger-action rules in order to identify their security risks. Using IFTTT as an exemplar platform, we irst enumerate the space of inter-rule vulnerabilities that exist within trigger-action platforms. To aid users in the identiication of these dangers, we go on to present iRuler, a system that performs Satisiability Modulo Theories (SMT) solving and model checking to discover inter-rule vulnerabilities within IoT deployments. iRuler operates over an abstracted information low model that represents the attack surface of an IoT deployment, but we discover in practice that such models are diicult to obtain given the closed nature of IoT platforms. To address this, we develop methods that assist in inferring triggeraction information lows based on Natural Language Processing. We develop a novel evaluative methodology for approximating plausible real-world IoT deployments based on the installation counts of 315,393 IFTTT applets, determining that 66% of the synthetic deployments in the IFTTT ecosystem exhibit the potential for interrule vulnerabilities. Combined, these eforts provide the insight into the real-world dangers of IoT deployment misconigurations. CCS CONCEPTS• Security and privacy → Formal methods and theory of security; Vulnerability scanners; Software security engineering; • Computing methodologies → Natural language processing; • Computer systems organization → Embedded and cyber-physical systems.

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“…For instance, prior systems designed to evaluate or enforce the safety/security of home automation have one trait in common: their reliance on safety/security properties that are manually specified, either by users (e.g., in case of Menshen [37] and AutoTAP [38]) or domain experts (e.g., IoTSAN [10], Soteria [4], IoTGuard [9], or ProvThings [7]). The practical policy specification approach enabled by H lion's scenarios (Sec.…”

Section: Related Workmentioning

confidence: 99%

Towards a Natural Perspective of Smart Homes for Practical Security and Safety Analyses

Manandhar

Moran

Kafle

et al. 2020

2020 IEEE Symposium on Security and Privacy (SP)

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Designing practical security systems for the smart home is challenging without the knowledge of realistic home usage. This paper describes the design and implementation of H lion, a framework that generates natural home automation scenarios by identifying the regularities in user-driven home automation sequences, which are in turn generated from routines created by end-users. Our key hypothesis is that smart home event sequences created by users exhibit inherent semantic patterns, or naturalness that can be modeled and used to generate valid and useful scenarios. To evaluate our approach, we first empirically demonstrate that this naturalness hypothesis holds, with a corpus of 30,518 home automation events, constructed from 273 routines collected from 40 users. We then demonstrate that the scenarios generated by H lion seem valid to end-users, through two studies with 16 external evaluators. We further demonstrate the usefulness of H lion's scenarios by addressing the challenge of policy specification, and using H lion to generate 17 security/safety policies with minimal effort. We distill 16 key findings from our results that demonstrate the strengths of our approach, surprising aspects of home automation, as well as challenges and opportunities in this rapidly growing domain.

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Systematically Ensuring the Confidence of Real-Time Home Automation IoT Systems

Cited by 30 publications

References 22 publications

IoTSafe: Enforcing Safety and Security Policy with Real IoT Physical Interaction Discovery

IoTSafe: Enforcing Safety and Security Policy with Real IoT Physical Interaction Discovery

Charting the Attack Surface of Trigger-Action IoT Platforms

Towards a Natural Perspective of Smart Homes for Practical Security and Safety Analyses

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