Cross-App Interference Threats in Smart Homes: Categorization, Detection and Handling

Chi, Haotian; Zeng, Qiang; Du, Xiaojiang; Yu, Junrong

doi:10.1109/dsn48063.2020.00056

Cited by 61 publications

(58 citation statements)

References 20 publications

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“…For example, by utilizing Abstract Syntax Tree (AST) analysis on SmartApps, [45] identifies requested and used capabilities in SmartApps, [10], [27] break down SmartApps and extract rule information, [46], [26], [11] build Deterministic Finite Automatons (DFAs) from SmartApps. Symbolic execution is a powerful technique to analyze rule semantics from apps [24], [25]. Text data crawling and natural language processing have been used for rule extraction from mobile apps and web pages [26], [47].…”

Section: ) Automation-dependent Data-minimization Policiesmentioning

confidence: 99%

“…Text data crawling and natural language processing have been used for rule extraction from mobile apps and web pages [26], [47]. Rather than develop new tools, in this paper, we adapt the solution provided in [24] to extract rules from SmartThings classic and manually encode rules defined in the new SmartThings and openHAB for which we are unable to find an open-source implementation. We envision that more rule extraction tools will be developed and made publicly available, which can eliminate the manual efforts for encoding rules.…”

Section: ) Automation-dependent Data-minimization Policiesmentioning

confidence: 99%

“…For instance, Jia et al [46] extensively surveyed app-level attacks on IoTs, and presented a context-based permission system. Other than single-app attacks, cross-app interaction (CAI) threats attracted the attention of recent works [71], [24], [25], [12], [11]. HomeGuard [71], [24] is the first work that systematically studies this problem and also the first one that proposes to use theorem proving to detect CAI threats.…”

Section: A Smart Home Securitymentioning

confidence: 99%

“…Other than single-app attacks, cross-app interaction (CAI) threats attracted the attention of recent works [71], [24], [25], [12], [11]. HomeGuard [71], [24] is the first work that systematically studies this problem and also the first one that proposes to use theorem proving to detect CAI threats. iRuler [72] applies the same approach to studying CAI threats on the IFTTT platform.…”

Section: A Smart Home Securitymentioning

confidence: 99%

“…Data minimization should not affect home automation. We observe that the semantics of home automation apps can be represented as rules in a trigger-conditionaction programming paradigm (code analysis [24], [25], [10] and natural language processing [10], [26], [27] have proven effective in extracting rules from apps). Our insight is that, according to the rule semantics, we could reduce data leakage without impairing automation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

Chi

Zeng

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

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Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFIREWALL, a customizable data-flow control system to enhance the privacy of IoT platform users. PFIREWALL automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFIRE-WALL provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFIREWALL transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFIREWALL reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.

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Section: ) Automation-dependent Data-minimization Policiesmentioning

confidence: 99%

Section: ) Automation-dependent Data-minimization Policiesmentioning

confidence: 99%

Section: A Smart Home Securitymentioning

confidence: 99%

Section: A Smart Home Securitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

Chi

Zeng

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

Self Cite

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A context‐aware internet of things‐driven security scheme for smart homes

Khanpara

Lavingia

Trivedi

et al. 2022

Security and Privacy

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In recent years, the Internet of Things (IoT) has become very popular as it has numerous applications in the industrial and research domain. Moreover, the features of IoT systems play a crucial role in the development of smart cities. It enables smart cities and their subsystems to monitor, control, and manage heterogeneous devices remotely by extracting and communicating real‐time data. However, automated IoT systems are vulnerable to many security threats like tempered protocols, device hijacking, and unauthorized access. Motivated by the aforementioned discussion, this paper addresses the security requirements of an essential subsystem of smart city architecture, that is, IoT‐based smart homes. Based on the features and functionalities of smart homes, the risk of security violations in the system behavior needs to be analyzed This paper explores various security threats in a smart home environment and proposes a context‐aware security‐based scheme to prevent and detect possible threats. Results show that the proposed scheme is superior compared to the traditional schemes considering parameters such as the performance, cost, and maintenance requirements.

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On the Security and Safety of AbU Systems

Pasqua

Miculan

2021

Software Engineering and Formal Methods

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Cross-App Interference Threats in Smart Homes: Categorization, Detection and Handling

Cited by 61 publications

References 20 publications

PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

A context‐aware internet of things‐driven security scheme for smart homes

On the Security and Safety of AbU Systems

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