The House That Knows You: User Authentication Based on IoT Data
Talha Ongun, Oliver Spohngellert, Alina Oprea, Cristina, Nita-Rotaru, Mihai Christodorescu, Negin Salajegheh

TL;DR
This paper introduces a novel behavioral authentication method for smart home IoT devices, achieving high accuracy in user identification by analyzing interaction patterns, thus addressing privacy and convenience issues of traditional methods.
Contribution
The study develops a new behavioral authentication approach based on IoT interaction features and demonstrates its effectiveness with a high-accuracy ensemble classifier in a real-world setting.
Findings
Ensemble classifier achieves 97% accuracy in user identification.
Behavioral features from IoT interactions effectively distinguish users.
Method offers a privacy-preserving alternative to traditional authentication.
Abstract
Home-based Internet of Things (IoT) devices have gained in popularity and many households have become 'smart' by using devices such as smart sensors, locks, and voice-based assistants. Traditional authentication methods such as passwords, biometrics or multi-factor (using SMS or email) are either not applicable in the smart home setting, or they are inconvenient as they break the natural flow of interaction with these devices. Voice-based biometrics are limited due to safety and privacy concerns. Given the limitations of existing authentication techniques, we explore new opportunities for user authentication in smart home environments. Specifically, we design a novel authentication method based on behavioral features extracted from user interactions with IoT devices. We perform an IRB-approved user study in the IoT lab at our university over a period of three weeks. We collect network…
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Taxonomy
TopicsUser Authentication and Security Systems · Advanced Malware Detection Techniques · Internet Traffic Analysis and Secure E-voting
