One particular trend observed in healthcare is the progressive shift of data and services to the cloud, partly due to convenience (e.g. availability of complete patient medical history in real-time) and savings (e.g. economics of healthcare data management). There are, however, limitations to using conventional cryptographic primitives and access control models to address security and privacy concerns in an increasingly cloud-based environment. In this paper, we study the potential to use the Blockchain technology to protect healthcare data hosted within the cloud. We also describe the practical challenges of such a proposition and further research that is required.

Internet of Things (IoT) plays an increasingly significant role in our daily activities, connecting physical objects around us into digital services. In other words, IoT is the driving force behind home automation, smart cities, modern health systems and advanced manufacturing. This also increases the likelihood of cyber threats against IoT devices and services. Attackers may attempt to exploit vulnerabilities in application protocols, including Domain Name System (DNS), Hyper Text Transfer Protocol (HTTP) and Message Queue Telemetry Transport (MQTT) that interact directly with backend database systems and client-server applications to store data of IoT services. Successful exploitation of one or more of these protocols can result in data leakage and security breaches. In this paper, an ensemble intrusion detection technique is proposed to mitigate malicious events, in particular botnet attacks against DNS, HTTP and MQTT protocols utilized in IoT networks. New statistical flow features are generated from the protocols based on an analysis of their potential properties. Then, an AdaBoost ensemble learning method is developed using three machine learning techniques, namely Decision Tree (DT), Naive Bayes (NB) and Artificial Neural Network (ANN), to evaluate the effect of these features and detect malicious events effectively. The UNSW-NB15 and NIMS botnet datasets with simulated IoT sensors’ data are used to extract the proposed features and evaluate the ensemble technique. The experimental results show that the proposed features have the potential characteristics of normal and malicious activity using the correntropy and correlation coefficient measures. Moreover, the proposed ensemble technique provides a higher detection rate and a lower false positive rate compared with each classification technique included in the framework and three other state-of-the-art techniques.