Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the "human firewall," which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs' security practices in various scenarios including big data. The intention is to determine the gap between staffs' security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs' security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies.

The novel coronavirus disease-19 (COVID-19) infection has altered the society, economy, and entire healthcare system. Whilst this pandemic has presented the healthcare system with unprecedented challenges, it has rapidly promoted the adoption of telemedicine to deliver healthcare at a distance. Telemedicine is the use of Information and Communication Technology (ICT) for collecting, organizing, storing, retrieving, and exchanging medical information. But it is faced with the limitations of conventional IP-based protocols which makes it challenging to provide Quality of Service (QoS) for telemedicine due to issues arising from network congestion. Likewise, medical professionals adopting telemedicine are affected with low QoS during health consultations with outpatients due to increased internet usage. Therefore, this study proposes a Software-Defined Networking (SDN) based telemedicine architecture to provide QoS during telemedicine health consultations. This study utilizes secondary data from existing research works in the literature to provide a roadmap for the application of SDN to improve QoS in telemedicine during and after the COVID-19 pandemic. Findings from this study present a practical approach for applying SDN in telemedicine to provide appropriate bandwidth and facilitate real time transmission of medical data.

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The menace of cyber attacks has become a concern for both the public and private sectors. Several approaches have been proposed to tackle the challenge, but an approach that has received widespread acceptance among cyber security professionals in both public and private sectors is cyber threat information (CTI) sharing. CTI refers to any information that can help an organisation identify, assess, monitor and respond to cyber threats. It includes indicators of compromise; tactics, techniques and procedures used by threat actors; suggested actions to detect, contain, or prevent attacks; and the findings from the analyses of incidents. Sharing CTI has been proposed as an efficient and effective way of improving overall cyber intelligence and defence. However, there are sources of liability that may dissuade private entities from participating in such sharing. The most cited source of liability is privacy and data protection law; although antitrust law, tort of negligence law and intellectual property law are also

In recent years, there has been an increase in the application of attribute-based access control (ABAC) in electronic health (e-health) systems. E-health systems are used to store a patient's electronic version of medical records. These records are usually classified according to their usage i.e., electronic health record (EHR) and personal health record (PHR). EHRs are electronic medical records held by the healthcare providers, while PHRs are electronic medical records held by the patients themselves. Both EHRs and PHRs are critical assets that require access control mechanism to regulate the manner in which they are accessed. ABAC has demonstrated to be an efficient and effective approach for providing fine grained access control to these critical assets. In this paper, we conduct a survey of the existing literature on the application of ABAC in e-health systems to understand the suitability of ABAC for e-health systems and the possibility of using ABAC access logs for observing, modelling and analysing security practices of healthcare professionals. We categorize the existing works according to the application of ABAC in PHR and EHR. We then present a discussion on the lessons learned and outline future challenges. This can serve as a basis for selecting and further advancing the use of ABAC in e-health systems.