Threat Detection in Trigger-Action Programming Rules of Smart Home With Heterogeneous Information Network Model

The increased utilization of Trigger-Action Programming (TAP) rules in smart homes has raised concerns regarding potential security threats in the interactions between smart digital devices/online services (DD/OS) and the physical environment. To ensure the secure use of intelligent and convenient infrastructure for users, we introduce an approach aimed at detecting potential security threats. In this paper, we propose IoT security threat models and categorize the threats into Risky DD/OS with Physical Security, Contradictory Operation of DD/OS and Environmental Impact Conflict. To effectively detect security threats, we construct an Internet of Things-Heterogeneous Information Networks (IoT-HIN) and enhance it with a knowledge base tool, transforming it into a knowledge-based IoT-HIN. We establish meta-paths to conduct analysis of events triggered by rules, and a threat detection algorithm is proposed to identify potential security threats and determine the rules leading to these threats. The proposed approach is validated using a real-world dataset, and the experimental results demonstrate its efficiency and practicality. Furthermore, a comparative analysis with similar works is conducted to highlight the superiority of our proposed approach.