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Dependable Wearable Systems
thesisposted on 09.12.2021, 16:37 by Edgardo A Barsallo YiEdgardo A Barsallo Yi
As wearable devices, like smartwatches and fitness monitors, gain popularity and are being touted for clinical purposes, evaluating the resilience and security of wearable operating systems (OSes) and their corresponding ecosystems becomes essential. One of the most dominant OSes for wearable devices is Wear OS, created by Google. Wear OS and Android (its counterpart OS for mobile devices) share similar features, but the unique characteristics and uses of wearable devices posses new challenges. For example, wearable applications are generally more dependent on device sensors, have complex communication patterns (both intra-device and inter-device), and are context-aware. Current research efforts on the Wear OS are more focused on the efficiency and performance of the OS itself, overlooking the resilience or security of the OS or its ecosystem.
This dissertation introduces a systematic analysis to evaluate the Wear OS's resilience and security. The work is divided into two main parts. First, we focus our efforts on developing novel tools to evaluate the robustness of the wearable OS and uncover vulnerabilities and failures in the wearable ecosystem. We provide an assessment and propose techniques to improve the system's overall reliability. Second, we turn our attention to the security and privacy of smart devices. We assess the privacy and security of highly interconnected devices. We demonstrate the feasibility of privacy attacks under these scenarios and propose a defense mechanism to mitigate these attacks.
For the resilience part, we evaluate the overall robustness of the Wear OS ecosystem using a fuzz testing-based tool [DSN2018]. We perform an extensive fault injection study by mutating inter-process communication messages and UI events on a set of popular wearable and mobile applications. The results of our study show similarities in the root cause of failures between Wear OS and Android; however, the distribution of exception differ in both OSes. Further, our study evidence that input validation has improved in the Android ecosystem with respect to prior studies. Then, we study the impact of the state of a wearable device on the overall reliability of the applications running in Wear OS [MobiSys2020]. We use distinguishable characteristics of wearable apps, such as sensor activation and mobile-wearable communication patterns, to derive a state model and use this model to target specific fuzz injection campaigns against a set of popular wearable apps. Our experiments revealed an abundance of improper exception handling on wearable applications and error propagation across mobile and wearable devices. Furthermore, our results unveiled a flawed design of the wearable OS, which caused the device to reboot due to excessive sensor use.
For the security and privacy part, we assess user awareness toward privacy risks under scenarios with multiple interconnected devices. Our results show that a significant majority of the users have no reservation while granting permission to their devices. Furthermore, users tend to be more conservative while granting permission on their wearables. Based on the results of our study, we demonstrate the practicability of leaking sensitive information inferred from the user by orchestrating an attack using multiple devices. Finally, we introduce a tool based on NLP (Natural Language Processing) techniques that can aid the user in detecting this type of attack.