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Inspired by the boom of the consumer IoT market, many device manufacturers,start-up companies and technology giants have jumped into the space.Unfortunately, the exciting utility and rapid marketization of IoT, come at theexpense of privacy and security. Industry reports and academic work haverevealed many attacks on IoT systems, resulting in privacy leakage, propertyloss and large-scale availability problems. To mitigate such threats, a fewsolutions have been proposed. However, it is still less clear what are theimpacts they can have on the IoT ecosystem. In this work, we aim to perform acomprehensive study on reported attacks and defenses in the realm of IoT aimingto find out what we know, where the current studies fall short and how to moveforward. To this end, we first build a toolkit that searches through massiveamount of online data using semantic analysis to identify over 3000 IoT-relatedarticles. Further, by clustering such collected data using machine learningtechnologies, we are able to compare academic views with the findings fromindustry and other sources, in an attempt to understand the gaps between them,the trend of the IoT security risks and new problems that need furtherattention. We systemize this process, by proposing a taxonomy for the IoTecosystem and organizing IoT security into five problem areas. We use thistaxonomy as a beacon to assess each IoT work across a number of properties wedefine. Our assessment reveals that relevant security and privacy problems arefar from solved. We discuss how each proposed solution can be applied to aproblem area and highlight their strengths, assumptions and constraints. Westress the need for a security framework for IoT vendors and discuss the trendof shifting security liability to external or centralized entities. We alsoidentify open research problems and provide suggestions towards a secure IoTecosystem.

Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be