This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

The popularity of Internet of Things (IoT) devices in smart homes has raised significant concerns regarding data security and privacy. Traditional machine learning (ML) methods for anomaly detection often require sharing sensitive IoT data with a central server, posing security and efficiency challenges. In response, this paper introduces FedGroup, a novel Federated Learning (FL) method inspired by FedAvg. FedGroup revolutionizes the central model’s learning process by updating it based on the learning patterns of distinct groups of IoT devices. Our experimental results demonstrate that FedGroup consistently achieves comparable or superior accuracy in anomaly detection when compared to both federated and non-federated learning methods. Additionally, Ensemble Learning (EL) collects intelligence from numerous contributing models, leading to enhanced prediction performance. Furthermore, FedGroup significantly improves the detection of attack types and their details, contributing to a more robust security framework for smart homes. Our approach demonstrates exceptional performance, achieving an accuracy rate of 99.64% with a minimal false positive rate (FPR) of 0.02% in attack type detection, and an impressive 99.89% accuracy in attack type detail detection.

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.