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Among all Internet of Things (IoT) devices, a subset of them are related to users. Leveraging these user-related IoT elements, it is possible to ensure the identity of the user for a period of time, thus avoiding impersonation. This need is known as Continuous Authentication (CA). Since 2009, a plethora of IoT-based CA academic research and industrial contributions have been proposed. We offer a comprehensive overview of 58 research papers regarding the main components of such a CA system. The status of the industry is studied as well, covering 32 market contributions, research projects, and related standards. Lessons learned, challenges, and open issues to foster further research in this area are finally presented. [ABSTRACT FROM AUTHOR]

Smart city–based IoT devices enable collection of vast amounts of data, which can be used to provide more efficient public and private services. Among these, healthcare is especially relevant, and smart health (s-health) models are already being deployed. The authors propose attribute-based credentials (ABCs) to cope with s-health privacy issues and to set the stage for the further adoption in other privacy-aware IoT-based smart cities’ services. [ABSTRACT FROM AUTHOR]

The permanent availability and relative obscurity of blockchains is the perfect ground for using them for malicious purposes. However, the use of blockchains by malwares has not been characterized yet. This paper analyses the current state of the art in this area. One of the lessons learned is that covert communications for malware have received little attention. To foster further defence-oriented research, a novel mechanism (dubbed Smart-Zephyrus) is built leveraging smart contracts written in Solidity. Our results show that it is possible to hide 4 Kb of secret in 41 s. While being expensive (around USD 1.82 per bit), the provided stealthiness might be worth the price for attackers. [ABSTRACT FROM AUTHOR]

Smartphones are equipped with a set of sensors that describe the environment (e.g., GPS, noise, etc.) and their current status and usage (e.g., battery consumption, accelerometer readings, etc.). Several works have already addressed how to leverage such data for user-in-a-context continuous authentication, i.e., determining if the porting user is the authorized one and resides in his regular physical environment. This can be useful for an early reaction against robbery or impersonation. However, most previous works depend on assisted sensors, i.e., they rely upon immutable elements (e.g., cell towers, satellites, magnetism), thus being ineffective in their absence. Moreover, they focus on accuracy aspects, neglecting usability ones. For this purpose, in this paper, we explore the use of four non-assisted sensors, namely battery, transmitted data, ambient light and noise. Our approach leverages data stream mining techniques and offers a tunable security-usability trade-off. We assess the accuracy, immediacy, usability and readiness of the proposal. Results on 50 users over 24 months show that battery readings alone achieve 97.05% of accuracy and 81.35% for audio, light and battery all together. Moreover, when usability is at stake, robbery is detected in 100 s for the case of battery and in 250 s when audio, light and battery are applied. Remarkably, these figures are obtained with moderate training and storage needs, thus making the approach suitable for current devices. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including internet of things, crowdsourcing, and smart homes.