# Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach

arXiv: https://arxiv.org/abs/2406.03836 · HTML: https://mlsanity.com/paper/2406.03836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Latent Attention For If-Then Program Synthesis](https://mlsanity.com/paper/1611.01867/) (2016)
- [Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1808.06740/) (2018)
- [Cross-App Interference Threats in Smart Homes: Categorization, Detection and Handling](https://mlsanity.com/paper/1808.02125/) (2018)
- [Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis](https://mlsanity.com/paper/2002.03485/) (2020)
- [Conflict Detection in IoT-based Smart Homes](https://mlsanity.com/paper/2107.13179/) (2021)
- [Discovering IoT Physical Channel Vulnerabilities](https://mlsanity.com/paper/2102.01812/) (2021)
- [TAPInspector: Safety and Liveness Verification of Concurrent Trigger-Action IoT Systems](https://mlsanity.com/paper/2102.01468/) (2021)

## Abstract (arXiv)

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.