The proliferation of the Internet of Things (IoT) requires establishing and maintaining secure communication between smart devices to ensure user privacy and trustworthiness of IoT systems. Zero-interaction pairing (ZIP) and zero-interaction authentication (ZIA) are recent techniques that allow pairing or authenticating devices without user involvement utilizing devices’ physical context (e.g., ambient audio). Compared to centralized security solutions for the IoT such as public-key infrastructure (PKI) and
conventional user-assisted pairing and authentication methods (e.g., entering a password), ZIP and ZIA schemes promise improved user experience, as they do not require users to participate in pairing or authentication procedures, and easy deployment, as they rely on on-board sensors of smart devices. However, we find that proposed ZIP
and ZIA schemes are still immature, requiring improvements in three areas: security, usability, and deployability. In this thesis, we advance the domain of ZIP and ZIA in these three areas as follows. First, we analyze state-of-the-art ZIP and ZIA schemes both theoretically and empirically using real-world data that we collect. Our findings reveal that these schemes show reduced security and usability under realistic conditions, and we identify reasons why this reduction occurs. Second, we improve on ZIP, proposing a novel ZIP architecture called FastZIP combining a recently introduced Fuzzy Password-Authenticated Key Exchange (fPAKE) protocol, which has stronger security properties than the cryptographic primitives used by the state-of-the-art ZIP schemes, and sensor
fusion, which allows building robust context from multiple sensor modalities, each capturing a distinct physical phenomenon. We demonstrate, collecting real-world data using off-the-shelf devices, that FastZIP has higher security guarantees than state-of-the-art ZIP schemes against brute-force offline and predictable context attacks (e.g., context
replay) and significantly shorter pairing time, improving the usability of our scheme. Third, we develop a new copresence detection method named Next2You; copresence detection is a core part of any ZIA scheme. Next2You utilizes channel state information (CSI), which captures a unique wireless context of an environment (e.g., a room), and neural networks. Through our real-world experiments using off-the-shelf smartphones, we demonstrate that Next2You outperforms state-of-the-art copresence detection methods in two ways: (1) it achieves accurate copresence detection in challenging cases of low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), thus is more secure and (2) Next2You requires devices to only have ubiquitous Wi-Fi chipsets, without a need for extra sensors (e.g., microphones), improving the deployability of our method. Fourth, we publicly release the collected context data and codebase of the above contributions, enhancing the reproducibility in the domain of ZIP and ZIA.