Security and Privacy for Emerging IoT Systems

Currently, there are many variations and versions of IoT (e.g., Internet of People (IoP), Internet of Sheep (IoS), Internet of Tomato (IoTo), Internet of Drones (IoD), Internet of Sports (IoS), etcetera), to name a few. The rapid advancement in related “data generation/production” tools and technologies have enabled the speedy transformation from “metadata” to “big data”, and lately given rise to the mind-blowing volume of the so-called “megadata”. Megadata necessitates burdensome resources and capabilities in order to aggregate, analyze, visualize and harvest knowledge from collected data. Besides, megadata emitted from the above-mentioned IoT-oriented applications and domains comes in different shapes, sizes, presentations, and formats – depending purely on the application domain and system features. For example, in Internet of Tomato (IoTo), data concerns about best time of implantation of tomato seeds, growth, and the harvesting season etc; whereas Internet of Sheep (IoS) alarms shepherds how much a sheep eats and drinks, or if a sheep is close-by the fence. Obviously, there is no direct link between the two applications of IoTo and IoS, and their produced data – they work in silos. To address the above limitations, the Internet of Cognitive Things (IoCT) has been introduced in a bid to allow various ‘sensors-empowered IoT-enabled’ objects/systems with cognitive capabilities, such as reasoning, learning, explaining and acting, to work collaboratively together. This implies that things can build up dynamic communities with their peers’ systems in a business context as/when necessary to respond to emergent situations. The on-trend IoCT works at unlocking the siloed data via exploiting data analysis and mining algorithms of Machine Learning (ML), Artificial Intelligence (AI), deep and reinforcement learning. Thereof, the IoCT promotes obtaining conversant results for consumers as and when required. Notwithstanding, there are still many overlooked issues and challenges that hinder the full realize and benefits of ML and AI for IoCT big data analysis, mining, processing, and prediction. The aim of this Topical Collection is to seek the latest research results from academia, industry, and government research labs and report their findings on selected areas of Security and Privacy for IoT Systems. Its goal is to share breakthrough ideas and research findings in both theory and practice within the research community. The Topical Collection aims to cover topics that include, but are not limited to, the following: (a) Access Control for IoT Systems; (b) Privacy Aware IoT Systems; (c) Combating Attacks to IoT Systems; (d) Security and Privacy for special kings of IoT systems (e.g., Internet of Transportation Systems, Industrial IoT Systems, Internet of Sports); (e) Security and Privacy Issues in heterogeneous IoT systems; (f) ML and AI Technologies for Secure IoT Systems; (g) Secure Authentication in IoT Systems; (h) Trust management in IoT systems; (i) Adversarial Machine Learning for attacks to IoT Systems; (j) Usable security and privacy for Secure IoT Systems; (k) Risk-based Secure and Private IoT Systems; (l) Behvioural Aspects of Secure IoT Systems; (m) Security and Privacy for IoT Sytems for Healhtcare, Manufacturing, Agriculture and other Applications. The Keywords are: IoT Systems, Security, Privacy, Accsss Control, Cyber Attacks, Adversarial Machine Learning