Since blockchain was introduced through Bitcoin, research has been ongoing to extend its applications to non-financial use cases. Healthcare is one industry in which blockchain is expected to have significant impacts. Research in this area is relatively new but growing rapidly; so, health informatics researchers and practitioners are always struggling to keep pace with research progress in this area. This paper reports on a systematic review of the ongoing research in the application of blockchain technology in healthcare. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and a systematic mapping study process, in which a well-designed search protocol is used to search four scientific databases, to identify, extract and analyze all relevant publications. The review shows that a number of studies have proposed different use cases for the application of blockchain in healthcare; however, there is a lack of adequate prototype implementations and studies to characterize the effectiveness of these proposed use cases. The review further highlights the state-of-the-art in the development of blockchain applications for healthcare, their limitations and the areas for future research. To this end, therefore, there is still the need for more research to better understand, characterize and evaluate the utility of blockchain in healthcare.

The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

Discovering Web services using keyword-based search techniques offered by existing UDDI APIs (i.e. Inquiry API) may not yield results that are tailored to clients' needs. When discovering Web services, clients look for those that meet their requirements, primarily the overall functionality and quality of service (QoS). Standards such as UDDI, WSDL, and SOAP have the potential of providing QoS-aware discovery, however, there are technical challenges associated with existing standards such as the client's ability to control and manage discovery of Web services across accessible service registries. This paper proposes a solution to this problem and introduces the Web service relevancy function (WsRF) used for measuring the relevancy ranking of a particular Web service based on client's preferences, and QoS metrics. We present experimental validation, results, and analysis of the presented ideas.

Synthetic data are increasingly being recognized for their potential to address serious real-world challenges in various domains. They provide innovative solutions to combat the data scarcity, privacy concerns, and algorithmic biases commonly used in machine learning applications. Synthetic data preserve all underlying patterns and behaviors of the original dataset while altering the actual content. The methods proposed in the literature to generate synthetic data vary from large language models (LLMs), which are pre-trained on gigantic datasets, to generative adversarial networks (GANs) and variational autoencoders (VAEs). This study provides a systematic review of the various techniques proposed in the literature that can be used to generate synthetic data to identify their limitations and suggest potential future research areas. The findings indicate that while these technologies generate synthetic data of specific data types, they still have some drawbacks, such as computational requirements, training stability, and privacy-preserving measures which limit their real-world usability. Addressing these issues will facilitate the broader adoption of synthetic data generation techniques across various disciplines, thereby advancing machine learning and data-driven solutions.

Cybersecurity is important today because of the increasing growth of the Internet of Things (IoT), which has resulted in a variety of attacks on computer systems and networks. Cyber security has become an increasingly difficult issue to manage as various IoT devices and services grow. Malicious traffic identification using deep learning techniques has emerged as a key component of network intrusion detection systems (IDS). Deep learning methods have been a research focus in network intrusion detection. A Recurrent Neural Network (RNN) is useful in a wide range of applications. First, this paper proposes a novel deep learning model for anomaly detection in IoT networks using a recurrent neural network. Long Short Term Memory (LSTM), BiLSTM, and Gated Recurrent Unit (GRU) techniques are used to implement the proposed model for anomaly detection in IoT networks. A Convolutional Neural Network (CNN) can analyze input features without losing important information, making them particularly well suited for feature learning. Next, a hybrid deep learning model was proposed using convolutional and recurrent neural networks. Finally, a lightweight deep learning model for binary classification was proposed using LSTM, BiLSTM, and GRU based approaches. The proposed deep learning models are validated using NSLKDD, BoT-IoT, IoT-NI, IoT-23, MQTT, MQTTset, and IoT-DS2 datasets. Compared to current deep learning implementations, the proposed multiclass and binary classification model achieved high accuracy, precision, recall, and F1 score.