Emerging risks in the IoT ecosystem: Who's afraid of the big bad smart fridge?

Abstract

Rapid technological innovations, including the emergence of the Internet of Things (IoT), introduce a range of uncertainties, opportunities, and risks. While it is not possible to accurately foresee IoT's myriad ramifications, futures and foresight methodologies allow for the exploration of plausible futures and their desirability. Drawing on the futures and foresight literature, the current paper employs a standardised expert elicitation approach to study emerging risk patterns in descriptions of IoT risk scenarios. We surveyed 19 IoT experts between January and February 2018 using an online questionnaire. The submitted scenarios provided expert's perception of evolving IoT risk trajectories and were evaluated using thematic analysis, a method used to identify and report patterns within data. Four common themes were extracted: physical safety; crime and exploitation; loss of control; and social norms and structures. These themes provide suitable analytical tools to contextualise emerging risks and help detecting gaps about security and privacy challenges in the IoT.

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

“…This is an important methodological principle which distinguishes out work within the cybersecurity domain. Recent Table 1 AI/ML algorithm application for descriptive, predictive, and prescriptive risk analytics in edge computing literature confirms diverse cyber risks from IoT systems (Maple 2017), including risks in IoT ecosystems (Tanczer et al 2018) and IoT environments (Breza et al 2018), such as risk from smart homes (Eirini Anthi et al 2019;Ghirardello et al 2018), the Industrial IoT (Boyes et al 2018), and challenges in security metrics (Agyepong et al 2019). Cybersecurity solutions for specific IoT risks are also emerging at a fast rate, such as new models on opportunities and motivations for reducing cyber risk (Safa et al 2018), adaptive intrusion detection (E. Anthi et al 2018), security economic by design (Craggs and Rashid 2017), highlighting the privacy requirements (Anthonysamy et al 2017) and strategies for achieving privacy (Van Kleek et al 2018).…”

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

“…Cyber risk traditionally emerges from human-computer interactions (Craggs and Rashid 2017), and the impact analysis result with different estimated loss ranges (Radanliev et al 2018). The cyber risk investigated in this study emerge from compiling of connected systems, creating risk from data in transit (Anthi et al 2018) and necessitating standardisation of methods (Tanczer et al 2018). The methodological approach used for risk quantification in this study is compliant with a Goal-Oriented Approach and Network-based Linear Dependency Modelling.…”

Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

“…How to integrate cyber recovery planning into supply chain management The I4.0 brings inherent cyber risks and digital supply chains require cyber recovery plans supported with machine learning, enabling machines to perform autonomous decisions (Tanczer et al 2018) and a design support system (Lee et al 2019a). To improve the response and recovery planning, digital supply chains need to be supported by feedback and control mechanisms, supervisory control of actions (Safa et al 2018).…”