Explore the vast range of titles available.

Distributed control systems (DCS) have revolutionized the communication process and attracted more interest due to their pervasive computing nature (cyber/physical), their monitoring capabilities and the benefits they offer. However, due to distributed communication, flexible network topologies and lack of central control, the traditional security strategies are inadequate for meeting the unique characteristics of DCS. Moreover, malicious and untrustworthy nodes pose a significant threat during the formation of a DCS network. Trust-based secure systems not only monitor and track the behavior of the nodes but also enhance the security by identifying and isolating the malicious node, which reduces the risk and increases network lifetime. In this research, we offer TRUSED, a trust-based security evaluation scheme that both, directly and indirectly, estimates each node’s level of trustworthiness, incorporating the cumulative trust concept. In addition, simulation results show that the proposed technique can effectively identify malicious nodes, determine their node’s trustworthiness rating, and improve the packet delivery ratio.

Smart healthcare environments are growing at a rapid pace due to the services and benefits offered to healthcare practitioners and to patients. At the same time, smart healthcare environments are becoming increasingly complex environments where a plethora of devices are linked with each other, to deliver services to patients, and they require special security measures to protect the privacy and integrity of user data. Moreover, these environments are exposed to various kinds of security risks, threats, and attacks. Firewalls are considered as the first line of defense for securing smart healthcare networks and addressing the challenges mentioned above. Firewalls are applied at different levels in networks, and range from conventional server-based to cloud-based firewalls. However, the selection and implementation of a proper firewall to get the maximum benefit is a challenging task. Therefore, understanding firewall types, the services offered, and analyzing underlying vulnerabilities are important design considerations that need addressing before implementing a firewall in a smart healthcare environment. The paper provides a comprehensive review and best practices of firewall types, with offered benefits and drawbacks, which may help to define a comprehensive set of policies for smart healthcare devices and environments.

Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficiency, and scalability, particularly in resource-constrained IoT environments. This study aims to create and assess a federated learning (FL) framework that integrates with long short-term memory (LSTM) networks for efficient intrusion detection in IoT-based WSNs. We design the framework to enhance detection accuracy, minimize false positive rates (FPR), and ensure data privacy, while maintaining system scalability. Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. The proposed model was tested on three widely used datasets: WSN-DS, CIC-IDS-2017, and UNSW-NB15. The evaluation metrics for its performance included accuracy, F1 score, FPR, and root mean square error (RMSE). We evaluated the performance of the FL-based LSTM model against traditional centralized models, finding significant improvements in intrusion detection. The FL-based LSTM model achieved higher accuracy and a lower FPR across all datasets than centralized models. It effectively managed sequential data in WSNs, ensuring data privacy while maintaining competitive performance, particularly in complex attack scenarios. FL and LSTM networks work well together to make a strong way to find intrusions in IoT-based WSNs, which improves both privacy and detection. This study underscores the potential of FL-based systems to address key challenges in IoT security, including data privacy, scalability, and performance, making the proposed framework suitable for real-world IoT applications.

Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user’s security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms.

Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anomalies, sensor faults, and malicious cyber interference. In this work, we propose a hybrid fusion framework designed to attribute the most plausible source of an anomaly, thereby supporting more reliable clinical decisions. The proposed framework is developed and evaluated using two complementary datasets: CICIoMT2024 for modelling security threats and a large-scale intensive care cohort from MIMIC-IV for analysing key vital signs and bedside interventions. The core of the system combines a supervised XGBoost classifier for attack detection with an unsupervised LSTM autoencoder for identifying physiological and technical deviations. To improve clinical realism and avoid artefacts introduced by quantised or placeholder measurements, the physiological module incorporates quality-aware preprocessing and missingness indicators. The fusion decision policy is calibrated under prudent, safety-oriented constraints to limit false escalation. Rather than relying on fixed fusion weights, we train a lightweight fusion classifier that combines complementary evidence from the security and clinical modules, and we select class-specific probability thresholds on a dedicated calibration split. The security module achieves high cross-validated performance, while the clinical model captures abnormal physiological patterns at scale, including deviations consistent with both acute deterioration and data-quality faults. Explainability is provided through SHAP analysis for the security module and reconstruction-error attribution for physiological anomalies. The integrated fusion framework achieves a final accuracy of 99.76% under prudent calibration and a Matthews Correlation Coefficient (MCC) of 0.995, with an average end-to-end inference latency of 84.69 ms (p95 upper bound of 107.30 ms), supporting near real-time execution in edge-oriented settings. While performance is strong, clinical severity labels are operationalised through rule-based proxies, and cross-domain fusion relies on harmonised alignment assumptions. These aspects should be further evaluated using realistic fault traces and prospective IoMT data. Despite these limitations, the proposed framework offers a practical and explainable approach for IoMT-based patient monitoring.

Purpose: This study aims to explore the cybersecurity threats and vulnerabilities unique to Industry 5.0, emphasizing the need for a robust security framework to protect human-machine interactions and industrial systems. Study design/methodology/approach: This review adopts a systematic analysis of existing literature, identifying security risks, challenges, and potential countermeasures within Industry 5.0. The study synthesizes findings from peer-reviewed journals, industry reports, and case studies to provide a comprehensive assessment of cybersecurity concerns in this emerging paradigm. Sample and data: The study evaluates cybersecurity trends and vulnerabilities based on recent empirical research, industry reports, and case studies from multiple sectors implementing Industry 5.0 technologies. Results: The review identifies key cybersecurity challenges, including an expanded attack surface, privacy risks, and the exploitation of intelligent systems. It underscores the need for adaptive and proactive security strategies tailored to the human-centric nature of Industry 5.0. Additionally, it presents actionable recommendations for securing industrial ecosystems, ensuring data integrity, and mitigating potential cyber threats. Originality/value: This study contributes to the growing body of knowledge on cybersecurity in Industry 5.0 by offering a structured framework for analyzing threats and vulnerabilities. It provides valuable insights for policymakers, industry leaders, and researchers, facilitating the development of secure, resilient, and ethically grounded industrial ecosystems. Research limitations/implications: While this review provides a comprehensive assessment of cybersecurity risks in Industry 5.0, future research should focus on empirical validations, real-world case studies, and the practical implementation of recommended security measures.

Vehicular ad hoc networks are new and emerging technology and special class of mobile ad hoc networks that provide wireless communication between vehicles without any fixed infrastructure. Geographical routing has appeared as one of the most scalable and competent routing schemes for vehicular networks. A number of strategies have been proposed for forwarding the packets in geographical direction of the destination, where information of direct neighbors is gained through navigational services. Due to dynamically changing topologies and high mobility neighbor information become outdated. To address these common issues in network different types of forwarding strategies have been proposed. In this review paper, we concentrate on beaconless forwarding methods and their forwarding methods in detail.

Digital transformation in Oman has amplified the risk of cyberattacks, as evidenced by millions of attacks targeting government websites and critical infrastructures. However, Oman's current cybersecurity preparedness, particularly in policy development, is inadequate, as indicated by its moderate ranking in the National Cyber Security Index and score of 0% in cybersecurity policy development. Furthermore, prior research has not extensively covered the implementation of information security standards within Omani ministries. This study addresses this critical gap by evaluating the current state of information security policy implementation across 18 Omani ministries. A survey of 36 IT and security managers assessed current policies and practices, focusing on confidentiality, integrity, and availability. The study highlights that, although fundamental information security principles are implemented, a limited number of ministries possess international certifications. Specific deficiencies exist in advanced security measures, particularly in inadequate authentication protocols, inadequate encryption of sensitive data, and insufficient disaster recovery plans. The outcomes of this research highlight the critical necessity for Omani government agencies to embrace innovative technologies and adhere to internationally recognised information security standards to improve their security stance. This study provides significant information for policymakers and professionals aiming to strengthen the security posture of Oman's public sector.

The global scarcity of irrigation water poses a significant challenge to the sustainable production of rice and its availability worldwide. With a growing population driving increased demand for rice, it is crucial to enhance rice production while minimizing water usage. Achieving this requires a comprehensive understanding of the complex interactions between water and nitrogen dynamics and the formulation of strategies to optimize the application of irrigation water and nitrogen fertilizers. This study aims to investigate the impact of varying irrigation regimes and nitrogen application rates on rice growth attributes, yield performance, overall crop productivity, and economic returns. In the 2021 and 2022 rice growing season, two field experiments were carried out in split plot design with four nitrogen levels in sub plots [N0: Control, N1: 75% RDN (Recommended dose of nitrogen; @ 120 kg N ha −1 ), N2: 100% RDN, and N3: 125% RDN] and four irrigation treatments in main plots [I1: recommended irrigation scheduling, I2: at field capacity (20 L m −2 ), I3: 10% depletion from field capacity (20 L m −2 ), and I4: 20% depletion from field capacity (20 L m −2 ). The experiments were replicated three times. The suggested irrigation scheduling treatment (flooded) showed improved growth characteristics, such as plant height, dry matter accumulation, leaf area index, tiller count, SPAD (Soil Plant Analysis Development) value, NDVI (Normalized Difference Vegetation Index) value, leaf relative water content, and yield attributes; however, these were comparable to the application of irrigation water at field capacity. Due to improved plant growth and yield-attributing characteristics, the I1 treatment recorded the highest grain yield of 8.58 t ha −1 and 8.4 t ha −1 , although it was comparable to the I2 treatment, which had grain yields of 8.27 t ha −1 and 8.15 t ha −1 in 2021 and 2022. The grain yield reported by the N3 treatment were significantly greater than those of the N2 treatment, IN 2021 and 2022 respectively. Applying nitrogen at 125% RDN (Recommended dose of nitrogen) and irrigation water at field capacity produced the highest benefit–cost ratio (1.64), which was closely followed by the same irrigation regime and 100% RDN application (1.60 BC ratio). Comparable to irrigation at field capacity, the suggested irrigation schedule demonstrated enhanced growth features, yield attributes, productivity, and profitability. The best way to achieve the optimum growth, productivity, and profitability in transplanted rice was to provide irrigation water at field capacity and nitrogen @ 100% RDN.