  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.
