The Wearable Internet of Medical Things (WIoMT) is a collective term for all wearable medical devices connected to the internet to facilitate the collection and sharing of health data such as blood pressure, heart rate, oxygen level, and more. Standard wearable devices include smartwatches and fitness bands. This evolving phenomenon due to the IoT has become prevalent in managing health and poses severe security and privacy risks to personal information. For better implementation, performance, adoption, and secured wearable medical devices, observing users’ perception is crucial. This study examined users’ perspectives of trust in the WIoMT while also exploring the associated security risks. Data analysed from 189 participants indicated a significant variance (R² = 0.553) on intention to use WIoMT devices, which was determined by the significant predictors (95% Confidence Interval; p < 0.05) perceived usefulness, perceived ease of use, and perceived security and privacy. These were found to have important consequences, with WIoMT users intending to use the devices based on the trust factors of usefulness, easy to use, and security and privacy features. Further outcomes of the study identified how users’ security matters while adopting the WIoMT and provided implications for the healthcare industry to ensure regulated devices that secure confidential data.

Cybersecurity risks have become obstinate problems for critical water infrastructure management in Australia and worldwide. Water management in Australia involves a vast complex of smart technical control systems interconnected with several networks, making the infrastructure susceptible to cyber-attacks. Therefore, ensuring the use of security mechanisms in the control system modules and communication networks for sensors and actuators is vital. The statistics show that Australia is facing frequent cyber-attacks, most of which are either undetected or overlooked or require immediate response. To address these cyber risks, Australia has changed from a country with negligible recognition of attacks on critical infrastructure to a country with improved capability to manage cyber warfare. However, little attention is paid to reducing the risk of attacks to the critical water infrastructure. This study aims to evaluate Australia’s current cybersecurity attack landscape and the implemented controls for water infrastructure using a systematic literature review (SLR). This study also compares Australia in the context of global developments and proposes future research directions. The synthesis of the evidence from 271 studies in this review indicates the importance of managing security vulnerabilities and threats in SCADA water control systems, including the need to upgrade the contemporary water security architecture to mitigate emerging risks. Moreover, human resource development with a specific focus on security awareness and training for SCADA employees is found to be lacking, which will be essential for alleviating cyber threats to the water infrastructure in Australia.

The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation. Typical water quality assessment requires continuous monitoring of water and sediments at remote locations with labour-intensive laboratory tests to determine the degree of pollution. We propose an automated water quality assessment framework where we formalise a predictive model using machine learning to infer the water quality and level of pollution using collected water and sediments samples. Firstly, due to the sparsity of sample collection locations, the amount of sediment samples of water is limited, and the dataset is incomplete. Therefore, after an extensive investigation on various data imputation methods’ performance in water and sediment datasets with different missing data rates, we chose the best imputation method to process the missing data. Afterwards, the water sediment sample will be tagged as one of four levels of pollution based on some guidelines and then the machine learning model will use a specific technique named classification to find the relationship between the data and the final result. After that, the result of prediction can be compared to the real result so that it can be checked whether the model is good and whether the prediction is accurate. Finally, the research gave improvement advice based on the result obtained from the model building part. Empirically, we show that our best model archives an accuracy of 75% after accounting for 57% of missing data. Experimentally, we show that our model would assist in automatically assessing water quality screening based on possibly incomplete real-world data.

Cyber sextortion attacks are security and privacy threats delivered to victims online, to distribute sexual material in order to force the victim to act against their will. This continues to be an under-addressed concern in society. This study investigated social engineering and phishing email design and influence techniques in susceptibility to cyber sextortion attacks. Using a quantitative methodology, a survey measured susceptibility to cyber sextortion with a focus on four different email design cues. One-way repeated measures ANOVA, post hoc comparison tests, Friedman nonparametric test, and Spearman correlation tests were conducted with results indicating that attention to email source and title/subject line significantly increased individuals’ susceptibility, while attention to grammar and spelling, and urgency cues, had lesser influence. As such, the influence of these message-related factors should be considered when implementing effective security controls to mitigate the risks and vulnerabilities to cyber sextortion attacks.

The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.