Graph Learning for Interactive Threat Detection in Heterogeneous Smart Home Rule Data
Summary: Glint, a graph-learning system, detects interactive threats in heterogeneous smart-home rule data via ITGNN. Trains on data from five platforms; uses contrastive learning for detection and transfer learning to generalize, unveiling four threat types. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Guangjing Wang (Michigan State University)
- 2. Nikolay Ivanov (Michigan State University)
- 3. Bocheng Chen (Michigan State University)
- 4. Qi Wang (University of Illinois Urbana-Champaign)
- 5. ThanhVu Nguyen (George Mason University)
- 6. Qiben Yan (Michigan State University)
BibTeX Citation
@inproceedings{wang_sigmod23,
title = {{Graph Learning for Interactive Threat Detection in Heterogeneous Smart Home Rule Data}},
author = {Wang, Guangjing and Ivanov, Nikolay and Chen, Bocheng and Wang, Qi and Nguyen, ThanhVu and Yan, Qiben},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588956},
url = {https://dl.acm.org/doi/10.1145/3588956},
year = {2023}
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