Leveraging the heterogeneity of the internet of things devices to improve the security of smart environments
Keywords: biometrics
internet of things
cyber security

The growing number of devices that are being incorporated into the Internet of Things (IoT) environments leads to a wider presence of a variety of sensors, making these environments heterogeneous. However, the lack of standard input interfaces in such ecosystems poses a challenge in securing them. Among other existing vulnerabilities, the most prevalent are the lack of adequate access control mechanisms and the exploitation of cross-channel interactions between smart devices.
In order to tackle the first challenge, I propose a novel behavioral biometric system based on naturally occurring interactions with objects in smart environments. This system is designed to reduce the reliance on existing app-based authentication mechanisms of current smart home platforms and it leverages existing heterogeneous IoT devices to both identify and authenticate users without requiring any hardware modifications of existing smart home devices.
To be able to collect the data and evaluate this system, I introduce an end-to-end framework for remote experiments. Such experiments play an important role across multiple fields of studies, from medical science to engineering, as they allow for better representation of human participants and more realistic experimental environments, and ensure research continuity in exceptional circumstances, such as nationwide lockdowns. Yet cyber security has few standards for conducting experiments with human participants, let alone in a remote setting. This framework systematizes design and deployment practices while preserving realistic, reproducible data collection and the safety and privacy of participants.
Using this methodology, I conduct two experiments. The first one is a multi-user study taking place in six households composed of 25 participants. The second experiment involves 13 participants in a company environment and is used to study mimicry attacks on the biometric system proposed in this thesis. I demonstrate that this system can identify users in multi-user environments with an accuracy of at least 98% for a single object interaction without requiring any sensors on the object itself. I also show that it can provide seamless and unobtrusive authentication while remaining highly resistant to zero-effort, video, and in-person observation-based mimicry attacks. Even when at most 1% of the strongest type of mimicry attacks are successful, this system does not require the user to take out their phone to approve legitimate transactions in more than 80% of cases for a single interaction. This increases to 92% of transactions when interactions with more objects are considered.
To mitigate the second vulnerability, where an attacker exploits multiple heterogeneous devices in a chain such that each one triggers the next, I propose a novel approach that uses only dynamic analysis to examine such interactions in smart ecosystems. I use real-time device data to generate a knowledge graph that models the interactions between devices and enables the system to identify attack chains and vulnerable automations. I evaluate this approach in a smart home environment with 8 devices and 10 automations, with and without the presence of an active user. I demonstrate that such a system can accurately detect 10 cross-channel interactions that lead to 30 different cross-channel interaction chains in the unoccupied environment and 6 such interactions that result in 13 interaction chains in the occupied environment.