SAFE-TAP: Semantic-aware and fused embedding for TAP rule security detection - Kuang, Zhejun; Zhu, Yusheng; Sun, Dawen; Zhao, Jian; Xing, Yongheng; Wang, Feng; Sun, Lei (Neurocomputing (Amsterdam), 2025)
Trigger-Action Programming (TAP) has emerged as a widely adopted paradigm for enabling automated interoperability among IoT devices. Despite its convenience, TAP introduces significant security vulnerabilities. To address this issue, we propose SAFE-TAP, a novel framework for detecting malicious TAP rules that integrates global semantic understanding with temporal feature analysis. To further enhance the detection performance, we introduce an innovative data augmentation strategy that leverages Large Language Models (LLMs) to generate semantically consistent rule variations. This approach improves data set balance and enhances the generalizability of the model. Experimental results demonstrate that SAFE-TAP outperforms baseline methods, and the incorporation of LLM-based data augmentation significantly improves detection performance under imbalanced data scenarios.