Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
Nan Zhang, Soteris Demetriou, Xianghang Mi, Wenrui Diao, Kan Yuan,, Peiyuan Zong, Feng Qian, XiaoFeng Wang, Kai Chen, Yuan Tian, Carl A. Gunter,, Kehuan Zhang, Patrick Tague, Yue-Hsun Lin

TL;DR
This paper provides a comprehensive analysis of IoT security issues, reviewing attacks and defenses, identifying gaps between academic research and industry practices, and proposing a taxonomy to guide future security efforts.
Contribution
It introduces a semantic analysis toolkit and a machine learning-based clustering approach to analyze over 3000 IoT security articles, and proposes a taxonomy to organize IoT security challenges.
Findings
Security problems in IoT are still largely unresolved.
There is a gap between academic research and industry practices.
A taxonomy for IoT security helps identify research priorities.
Abstract
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 the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · IoT and Edge/Fog Computing · Blockchain Technology Applications and Security
