Industry 4.0 is fundamentally based on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency. Industry 4.0-based wireless sensor networks (WSNs) are an integral part of modern industrial operations however, these networks face escalating cybersecurity threats. These networks are always vulnerable to cyber-attacks as they continuously collect data and optimize processes. Increased connections make people more susceptible to cyberattacks, necessitating the use of strong cybersecurity measures to protect sensitive data. This study proposes a predictive framework intended to intelligently prioritize and prevent cybersecurity intrusions on WSNs in Industry 4.0. The proposed framework enhances the cybersecurity of WSNs in Industry 4.0 using a multi-criteria approach. It implements machine-learning and deep-learning algorithms for cybersecurity intrusion detection in WSNs of Industry 4.0 and provides prevention by assigning priorities to the threats based on the situation and nature of the attacks. We implemented three models, i.e., Decision Tree, MLP, and Autoencoder, as proposed algorithms in the framework. For multidimensional classification and detection of cybersecurity intrusions, we implemented Decision Tree and MLP models. For binary classification and detection of cybersecurity intrusions in WSNs of Industry 4.0, we implemented Autoencoder model. Simulation results show that the Decision Tree model provides an accuracy of 99.48%, precision of 99.49%, recall of 99.48%, and F1 score of 99.49% in the detection and classification of cybersecurity intrusions. The MLP model provides an accuracy of 99.52%, precision of 99.5%, recall of 99.5%, and F1 score of 99.5% in the detection and classification of cybersecurity intrusions. The implementation of Autoencoder with binary classification yields an accuracy of 91%, a precision of 92%, a recall of 91%, and an F1 score of 91%. The benchmark models, i.e., Random Forest (RF) for multidimensional classification and Logistic Regression (LR) for binary classification, have also been implemented. We compared the performance of the benchmark models with the models implemented in the proposed framework, revealing that the models in the proposed framework significantly outperformed the benchmark models. The framework presents an intelligent prioritizing methodology that is significant for effectively identifying and addressing high-risk intrusions. The proposed framework implements a proactive preventive system that functions as a strong defensive wall by quickly putting countermeasures in place to eliminate threats and increase network resilience.

Fake news, colloquially referred to as false news, exerts a profound influence on fundamental facets of our societal framework. Manual fact verification is a frequently utilized strategy for mitigating the harmful impacts of false news transmission. However, when assessing the massive volume of freshly produced material, manual fact verification is insufficient. Furthermore, the quantity of labeled datasets is limited, people are unreliable annotators, resources are largely in the English language, and they mostly concentrate on articles related to news. Cutting-edge deep learning algorithms are employed to deal with this challenge automatically and address these difficulties. Nevertheless, the large number of models and variability of characteristics utilized in the literature frequently constitute a barrier for researchers attempting to enhance the effectiveness of models. This paper introduces a model designed to address the issue of false news, with a focus on analyzing news headlines. The approach is rooted in a hybrid classification model where our model integrates a BERT architecture, and the outputs are seamlessly linked to bi-directional deep learning layers, including a bi-LSTM layer and a bi-GRU layer. The model's training and evaluation were conducted using the WELFake dataset, comprising four prevalent news databases. A comparative analysis was carried out using the proposed model and standard classification models. Additionally, standard machine learning and deep learning models along with vanilla BERT were trained on the same dataset with comparable constraints to assess the impact of integrating a bi-directional deep learning layer with BERT. The results revealed an improvement in accuracy, as our proposed model achieved an accuracy of 98.1% and an F1 score of 0.982.

The Internet of Vehicles (IoV) is a critical component of the smart city. Various nodes exchange sensitive data for urban mobility, such as identification, position, messages, speed, and traffic statistics. Along with developing smart cities come threats to privacy and security through networks. Security is of the highest priority, considering various security‐privacy risks from the wellness, safety, and confidentiality of men and women inside the vehicle. This survey presents a detailed analysis of state‐of‐the‐art and evolving security challenges to IoV systems. It handles security challenges, such as data integrity and privacy. It also includes a critical review of the literature to identify gaps in current security mechanisms. It uses complete mathematical modeling and case studies to show the practical effectiveness of the proposed solutions. It aims to guide future development and implementation of more secure, efficient, and resilient IoV systems, particularly in smart city environments. It also introduces a novel Intrusion Detection System (IDS) with Artificial Intelligence (AI), smart contracts, and blockchain technology. These smart contracts ensure instant security with the utmost level of vulnerability through blockchain technology. In addition, we proposed a hybrid multi‐layered framework using Fog to conserve the resources at the vehicle level. We used mathematical proof to assess this framework. Merging blockchain, smart contracts, and AI into IoVs could increase human security by removing significant vulnerabilities.

Health insurance has become a crucial component of people’s lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP’s applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting.

Healthcare is a critical field of research and equally important for all nations. Providing secure healthcare facilities to citizens is the primary concern of each nation. However, people living in remote areas do not get timely and sufficient healthcare facilities, even in developed countries. During the recent COVID-19 pandemic, many fatalities occurred due to the inaccessibility of healthcare facilities on time. Therefore, there is a need to propose a solution that may help citizens living in remote areas with proper and secure healthcare facilities without moving to other places. The revolution in ICT technologies, especially IoT, 5G, and cloud computing, has made access to healthcare facilities easy and approachable. There is a need to benefit from these technologies so that everyone can get secure healthcare facilities from anywhere. This research proposes a framework that will ensure 24/7 accessibility of healthcare facilities by anyone from anywhere, especially in rural areas with fewer healthcare facilities. In the proposed approach, the patients will receive doorstep treatment from the remote doctor in rural areas or the nearby local clinic. Healthcare resources (doctor, treatment, patient counseling, diagnosis, etc.) will be shared remotely with people far from these facilities. The proposed approach is tested using mathematical modeling and a case study, and the findings confirm that the proposed approach helps improve healthcare facilities for remote patients.

Cognitive networks with the integration of smart and physical devices are rapidly utilized for the development of smart cities. They are explored by many real-time applications such as smart homes, healthcare, safety systems, and other unpredictable environments to gather data and process network requests. However, due to the external conditions and inherent uncertainty of wireless systems, most of the existing approaches cannot cope with routing disturbances and timely delivery performance. Further, due to limited resources, the demand for a secure communication system raises another potential research challenge to protect sensitive data and maintain the integrity of the urban environment. This paper presents a secured decision-making model using reinforcement learning with the combination of blockchain to enhance the degree of trust and data protection. The proposed model increases the network efficiency for resource utilization and the management of communication devices with the alliance of security. It provides a reliable and more adaptive paradigm by exploring learning techniques for dealing with the intrinsic uncertainty and imprecision of cognitive systems. Also, the incorporation of blockchain technology reduces the risk of a single point of failure, malicious vulnerabilities, and data leakage, ultimately fostering trust for urban sensor applications. It validates the incoming routing links and identifies any communication fault incurred due to malicious interference. The proposed model is rigorously tested and verified using simulations and its significance has been proven for network metrics in comparison to existing solutions.