Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach
<|reference_start|>Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach: Emerging Internet of Things (IoT) platforms provide sophisticated capabilities to automate IoT services by enabling occupants to create trigger-action rules. Multiple trigger-action rules can physically interact with each other via shared environment channels, such as temperature, humidity, and illumination. We refer to inter-rule interactions via shared environment channels as a physical inter-rule vulnerability. Such vulnerability can be exploited by attackers to launch attacks against IoT systems. We propose a new framework to proactively discover possible physical inter-rule interactions from user requirement specifications (i.e., descriptions) using a deep learning approach. Specifically, we utilize the Transformer model to generate trigger-action rules from their associated descriptions. We discover two types of physical inter-rule vulnerabilities and determine associated environment channels using natural language processing (NLP) tools. Given the extracted trigger-action rules and associated environment channels, an approach is proposed to identify hidden physical inter-rule vulnerabilities among them. Our experiment on 27983 IFTTT style rules shows that the Transformer can successfully extract trigger-action rules from descriptions with 95.22% accuracy. We also validate the effectiveness of our approach on 60 SmartThings official IoT apps and discover 99 possible physical inter-rule vulnerabilities.<|reference_end|>
@article{huang2024proactive,
    title={Proactive Detection of Physical Inter-rule Vulnerabilities in IoT
  Services Using a Deep Learning Approach},
    author={Bing Huang, Chen Chen, Kwok-Yan Lam, Fuqun Huang},
    journal={arXiv preprint arXiv:2406.03836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03836},
    primaryClass={cs.CR cs.AI}
}