TAESim: A Testbed for IoT Security Analysis of Trigger-Action Environment
conference contribution
posted on 2024-11-03, 15:16authored byXinbo Ban, Ming Ding, Shigang Liu, Chao ChenChao Chen, Jun Zhang, Yang Xiang
The Internet of Things (IoT) networks promote significant convenience in every aspect of our life, including smart vehicles, smart cities, smart homes, etc. With the advancement of IoT technologies, the IoT platforms bring many new features to the IoT devices so that these devices can not only passively monitor the environment (e.g. conventional sensors), but also interact with the physical surroundings (e.g. actuators). In this light, new problems of safety and security arise due to the new features. For instance, the unexpected and undesirable physical interactions might occur among devices, which is known as inter-rule vulnerability. A few work have investigated the inter-rule vulnerability from both cyberspace and physical channels. Unfortunately, only few research papers take advantage of run-time simulation techniques to properly model trigger action environments. Moreover, no simulation platform is capable of modeling primary physical channels and studies the impacts of physical interactions on IoT safety and security. In this paper, we introduce TAESim, a simulation testbed to support reusable simulations in the research of IoT safety and security, especially for the IoT activities in home automation that could involve possibly unexpected interactions. TAESim operates over MATLAB/Simulink and constructs a digital twin for modeling the nature of the trigger-action environment using simulations. It is an open-access platform and can be used by the research community, government, and industry who work toward preventing the safety and security consequences in the IoT ecosystem. In order to evaluate the effectiveness and efficiency of the testbed, we conduct some experiments and the results show that the simulations are completed in a few seconds. We also present two case studies that can report unexpected consequences.