Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.

Industrial IoT (IIoT) is a novel concept of a fully connected, transparent, automated, and intelligent factory setup improving manufacturing processes and efficiency. To achieve this, existing hierarchical models must transition to a fully connected vertical model. Since IIoT is a novel approach, the environment is susceptible to cyber threat vectors, standardization, and interoperability issues, bridging the gaps at the IT/OT ICS (industrial control systems) level. IIoT M2M communication relies on new communication models (5G, TSN ethernet, self-driving networks, etc.) and technologies which require challenging approaches to achieve the desired levels of data security. Currently there are no methods to assess the vulnerabilities/risk impact which may be exploited by malicious actors through system gaps left due to improper implementation of security standards. The authors are currently working on an Industry 4.0 cybersecurity project and the insights provided in this paper are derived from the project. This research enables an understanding of converged/hybrid cybersecurity standards, reviews the best practices, and provides a roadmap for identifying, aligning, mapping, converging, and implementing the right cybersecurity standards and strategies for securing M2M communications in the IIoT.

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed.

This paper reviews the Internet of Things (IoT) in healthcare, also known as Healthcare-IoT (H-IoT), and its security and privacy challenges. Specifically, smartwatches, blood pressure sensors, and temperature sensors are discussed as IoT devices that pose vulnerabilities. Several risks may arise, such as data breaches, unauthorized access, and potential patient harm. This review explores data security challenges associated with machine learning and H-IoT. Additionally, it emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application layers. Detecting and responding to anomalies involve a wide range of cyber threats and attacks, as well as protocols such asWi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). Hence, to protect and mitigate H-IoT devices from the increasing cyber threat landscape, a robust authentication mechanism based on machine learning and deep learning techniques is required. Overall, this paper reviews the security and privacy challenges in healthcare IoT and discusses risk mitigation strategies for building cyber resilience in H-IoT.

Wind energy penetration has considerably increased in the recent past. However, wind turbines are often prone to various faults which may lead to failures causing huge production and economic losses with increased downtime. To reduce this production and economic loss. It is therefore clear that early detection of these failures can be achieved through an appropriate condition monitoring approach. Various approaches are reported for predicting the condition of wind turbines. However, deploying a costly condition monitoring system with additional data accusation devices poses a challenge for windfarm owners. To address this challenge this study employing Preferred Reporting Item for Systematic Literature Review and Meta Analysis (PRISMA) provides a detailed review of various approaches used for the wind turbine condition monitoring. The key objective of this study is to find out the most frequently used and reliable method of wind turbine condition monitoring, focusing particularly on the SCADA-based approach due to its practical advantages and widespread adoption in the industry. Additionally, this review considers the distinctive concept of machine learning model building which includes data input and its processing, feature selection, model building and its evaluation to analyze the research issues. The review findings concluded that amongst various condition monitoring techniques, SCADA based data driven approach is most popular as it does not require additional sensors, blade mount cameras, unmanned arial vehicles and a separate data accusation unit. Nevertheless, condition monitoring results based on SCADA approach to provide varying predications for differently located wind farms which is a pertinent knowledge gap. This review study provides some detailed insight into various condition monitoring approaches of wind turbines and recommendation to consider any of these based on available resources.

The Internet of Things (IoT) revolutionizes precision healthcare by enhancing patient care and reducing costs. However, this technology poses challenges in securing Healthcare-IoT (H-IoT) devices, as low-latency packet transmission increases vulnerability to attacks (e.g., Distributed Denial of Service (DDoS)) in 5G networks. This research develops a monitoring frequency-based detection and dynamic threshold mitigation method using Temporal Convolutional Networks (TCNs) in 5G H-IoT environments. A monitoring frequency-based detection method calculates each H-IoT node’s message count over the past five seconds as a percentage of total traffic. A dynamic threshold strategy also provides adaptive security by averaging detected malicious behavior messages across all nodes to determine the base threshold. This approach enhances classification accuracy, effectively mitigates actual DDoS attack nodes, and blacklists malicious nodes without false positives in H-IoT environments. Moreover, this research creates DDoS attack models, monitoring parameters using two simulators (Cooja and ns-3) on H-IoT devices. Data is collected over the Message Queuing Telemetry Transport (MQTT) and User Datagram Protocol (UDP) in a realistic 5G-based H-IoT environment, creating two datasets with malicious and benign data. The proposed TCN-based DDoS prediction and mitigation method achieves 99.98% and 95% accuracy on the MQTT dataset. This model also attains a slightly higher prediction accuracy of 99.99% and mitigation of 80% on the UDP dataset. This research evaluates the proposed model against prior methods, including Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), demonstrating improved accuracy. Thus, the proposed TCN model is a versatile and resilient security solution for H-IoT environments.