CCDF-TAP: A Context-Aware Conflict Detection Framework for IoT Trigger-Action Programming With Graph Neural Network

The rapid expansion of the Internet of Things (IoT) has led to the development of smart homes and automation systems. Trigger-action programming (TAP) has emerged as a prevalent paradigm used in IoT, facilitating the creation of automation rules. However, with the proliferation of TAP rules, the potential for conflicts between them grows significantly, which results in undesired outcomes or even safety risks. In this article, we propose a context-aware conflict detection framework for TAP rules, called CCDF-TAP, to identify the potential rule conflicts. Specifically, we incorporate external knowledge and context information during the TAP data preprocessing stage, which is conducive to accurately defining the rule conflicts. Then, based on the above information, the conflict types are defined and a conflict graph is constructed, which establishes a unified format for the rule conflict detection task. Finally, we propose a novel algorithm called dual-channel graph attention auto-encoders (DualGAAs) for efficient conflict detection, which takes the conflict graph as the input and excels in accurately identifying conflicts. Extensive experiments conducted on a comprehensive IFTTT data set demonstrate the superiority of DualGAA in detecting conflicts, achieving an exceptional accuracy of 98.85% and an F1 score of 98.91%. The contributions of our study offer a comprehensive end-to-end solution for context-aware conflict detection in TAP rules, thereby significantly enhancing the security and dependability of IoT smart home systems.