SoftAuthZ: A Context-Aware, Behavior-Based Authorization Framework for Home IoT
| Autor: | Saket Chandra, Yuval Elovici, Nirnay Ghosh, Vinay Sachidananda |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Authentication
Computer Networks and Communications business.industry Computer science Authorization 020206 networking & telecommunications Access control Context (language use) 02 engineering and technology Multi-factor authentication Permission Computer security computer.software_genre Computer Science Applications Insider Hardware and Architecture Home automation Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Internet of Things Everyday life computer Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 6:10773-10785 |
| ISSN: | 2372-2541 |
| Popis: | The smart home is one of the most prominent applications in the paradigm of the Internet of Things (IoT). While, it has added a level of comfort and convenience to our everyday life, at the same time, it brings a unique security challenge of mitigating insider threats, posed by legitimate users. Such threats primarily arise due to sharing of IoT devices and the presence of complex social and trust relationships among the users. The state-of-the-art home IoT platforms manage access control by deploying various multifactor authentication mechanisms. Nevertheless, such hard-security measures are inadequate to thwart insider threats, and there is a growing need to integrate user behavior and environmental contexts to make intelligent authorization decisions. In this article, we propose a novel context-sensitive and behavior-based security framework, called SoftAuthZ , that incorporates soft-security mechanisms, such as belief, confidence, etc., to support authorization decisions. Our framework integrates multiple IoT environment-specific attributes, such as environmental context, nature of the device, requested capabilities (actions), users’ trust levels concerning the home environment, and variability in device access requests into a linear regression model, and computes confidence related to access requests. Such confidence scores can be used by the home IoT platform to make authorization decisions. Extensive analysis and simulation-based performance evaluation validate the efficacy of our framework, demonstrating that it can classify users based on their device usages, and also achieve higher rates of successful authorization. |
| Databáze: | OpenAIRE |
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