IoTGaze: IoT Security Enforcement via Wireless Context Analysis
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Abstract (arXiv)
Internet of Things (IoT) has become the most promising technology for service automation, monitoring, and interconnection, etc. However, the security and privacy issues caused by IoT arouse concerns. Recent research focuses on addressing security issues by looking inside platform and apps. In this work, we creatively change the angle to consider security problems from a wireless context perspective. We propose a novel framework called IoTGaze, which can discover potential anomalies and vulnerabilities in the IoT system via wireless traffic analysis. By sniffing the encrypted wireless traffic, IoTGaze can automatically identify the sequential interaction of events between apps and devices. We discover the temporal event dependencies and generate the Wireless Context for the IoT system. Meanwhile, we extract the IoT Context, which reflects user's expectation, from IoT apps' descriptions and user interfaces. If the wireless context does not match the expected IoT context, IoTGaze reports an anomaly. Furthermore, IoTGaze can discover the vulnerabilities caused by the inter-app interaction via hidden channels, such as temperature and illuminance. We provide a proof-of-concept implementation and evaluation of our framework on the Samsung SmartThings platform. The evaluation shows that IoTGaze can effectively discover anomalies and vulnerabilities, thereby greatly enhancing the security of IoT systems.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2020.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2023ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attackscited by 6
- 2024Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Maskcited by 5
- 2026SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contextscited by 2
- 2024Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Surveycited by 0
- 2026SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts24 Feb 2026
- 2024Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask16 Jun 2024
- 2024Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey24 Mar 2024
- 2023ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks15 Feb 2023