Abstract Cyber-Physical Systems (CPS) operate in increasingly complex and security-critical environments where system faults, misconfigurations, and cyberattacks can compromise safety, availability, and operational integrity. This paper presents CROSS (Cross-platform Remediation and Observability Self-Healing System), a cloud-native, cross-platform approach that extends the self-healing paradigm beyond anomaly detection to encompass autonomous, security-aware remediation. Building upon the Log Intelligence and Self-Healing System ( LISH ) (Johnphill et al. 2023a), which utilised CountVectorizer and Multinomial Naive Bayes ( MNB ) for log-based anomaly classification, CROSS introduces a policy-driven remediation layer that executes context-specific recovery actions such as service restarts, system updates, device reboots, and configuration enforcement across Android, Linux, macOS, and Windows. Prometheus-based observability (Pai and Srinivas 2024) provides fine-grained telemetry on anomalies and remedial actions, enabling continuous monitoring, auditability, and adaptive security governance. Experimental evaluation demonstrates measurable reductions in mean time to recovery (MTTR) and improvements in anomaly containment and resilience across heterogeneous CPS environments. Although CROSS includes mechanisms that are applicable to cybersecurity scenarios, the present evaluation focuses on operational anomalies rather than explicit attack-induced behaviours. Accordingly, its cybersecurity relevance is framed as an architectural capability, with empirical security benchmarking identified as future work. The proposed approach bridges the gap between anomaly detection and active cyber defence, embedding explainable, automated remediation within the operational lifecycle of CPS.

The cloud-based Internet of Things (IoTs) storage enables patients to monitor their health remotely and offers services for physicians of various Medical Institutions (MIs) to diagnose and treat them on time. As a matter of trust, patients are legally expected to hide their real identity and ensure data privacy in the cross-domain of IoT-healthcare, whether it is stored correctly or modified due to external and internal attacks in the cloud. Additionally, physicians treat patients and continuously store duplicated data in cloud storage, which increases the cost of computing. In this context, this paper presents HIDE-Healthcare IoT Data privacy trust management framework, focusing on attributes. Patients’ attributes are used to encrypt and decrypt sensory data between patients and different entities by incorporating the idea of trustworthy and secure shared keys. HIDE uses an intelligent object’s pointer to store the same patient’s sensory data in various versions to prevent data duplication, which will help track MIs that treat patients. An intelligent content-based emergency data access control is developed to monitor multiple patient health criticalities in HIDE. The security analysis and experimental evaluation attest to the benefits of the proposed HIDE framework, considering security and privacy metrics.

The huge advancement in the field of communication has pushed the innovation pace toward a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility), and widely distributed network need to be carefully addressed. This article, therefore, focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new nonlinear Lévy Brownian generalized normal distribution optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining the trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and internode distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods.

Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimisation has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimisation tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimisation. In this paper, a conditional opposition-based particle swarm optimisation is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimisation. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes.

This paper is an influential attempt to identify and alleviate some of the issues with the recently proposed optimization technique called the Marine Predator Algorithm (MPA). With a visual investigation of its exploratory and exploitative behavior, it is observed that the transition of search from being global to local can be further improved. As an extremely cost-effective method, a set of nonlinear functions is used to change the search patterns of the MPA algorithm. The proposed algorithm, called Nonlinear Marin Predator Algorithm (NMPA), is tested on a set of benchmark functions. A comprehensive comparative study shows the superiority of the proposed method compared to the original MPA and even other recent meta-heuristics. The paper also considers solving a real-world case study around power allocation in non-orthogonal multiple access (NOMA) and visible light communications (VLC) for Beyond 5G (B5G) networks to showcase the applicability of the NMPA algorithm. NMPA algorithm also shows its superiority in solving a wide range of benchmark functions as well as obtaining fair power allocation for multiple users in NOMA-VLC-B5G systems compared with the state-of-the-art algorithms.1

The Internet of Vehicles has attracted a lot of attention in the automotive industry and academia recently. We are witnessing rapid advances in vehicular technologies that comprise many components, such as onboard units (OBUs) and sensors. These sensors generate a large amount of data, which can be used to inform and facilitate decision making (e.g., navigating through traffic and obstacles). One particular focus is for automotive manufacturers to enhance the communication capability of vehicles to extend their sensing range. However, the existing short-range wireless access, such as dedicated short-range communication (DSRC), and cellular communication, such as 4G, is not capable of supporting the high volume data generated by different fully connected vehicular settings. Millimeter-wave (mmWave) technology can potentially provide terabit data transfer rates among vehicles. Therefore, we present an in-depth survey of the existing research, published in the last decade, and we describe the applications of mmWave communications in vehicular communications. In particular, we focus on MAC and physical layers and discuss related issues, such as sensing-aware MAC protocol, handover algorithms, link blockage, and beamwidth size adaptation. Finally, we highlight various aspects related to smart transportation applications, and we discuss future research directions and limitations.

The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.

Software-defined Internet of Vehicles (SDIoV) has emerged as a promising field of study as it could overcome the shortcomings of traditional vehicular networks, such as offering efficient data transmission and traffic shaping in different vehicular scenarios to satisfy all the requirements of applications on the fly. Although routing solutions are lightly addressed for SDIoV, there are many limitations of routing protocols unaddressed in such environment. More precisely, shortest path routing algorithms are mostly focused in the state of the arts. This paper presents quality of service aware routing algorithm that forwards packets toward the most reliable and connected path to the destination. Particularly, candidate routes should satisfy metrics, such as signal to interference and noise ratio (SINR) constraint and have the highest probability of connectivity. To address these issues, we have formulated a discrete optimization problem to favor the best route among candidate paths and proposed the modified laying chicken algorithm (LCA) that results better results than the traditional approaches. We have mathematically analyzed the probability of connectivity along with the SINR metric. Moreover, a multiscore function based on traffic density and greediness factor is proposed to make intelligent decision at the intersections. Simulation results are used to validate the superiority of the proposed routing approach over the existing solutions.

Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research in the last few years. The major factor behind the research efforts in that field is their vast range of applications which include surveillance systems, military operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and energy constrained devices; therefore, energy‐efficient routing protocol is the foremost concern. In this paper, an energy‐efficient routing protocol for wireless sensor networks is proposed. Our protocol consists of a routing algorithm for the transmission of data, cluster head selection algorithm, and a scheme for the formation of clusters. On the basis of energy analysis of the existing routing protocols, a multistage data transmission mechanism is proposed. An efficient cluster head selection algorithm is adopted and unnecessary frequency of reclustering is exterminated. Static clustering is used for efficient selection of cluster heads. The performance and energy efficiency of our proposed routing protocol are assessed by the comparison of the existing routing protocols on a simulation platform. On the basis of simulation results, it is observed that our proposed routing protocol (EE‐MRP) has performed well in terms of overall network lifetime, throughput, and energy efficiency.

Abstract Locating sources of distributed attack is time‐consuming; attackers are identified long after the attack is completed. This paper proposes a trackback model for identifying attackers and locating their distributed sources in real time. Attackers are identified by monitoring violations of malicious end users on their bandwidth shares predefined in the service level agreement. Then, active connections of the malicious users are investigated to locate the host machines used as distributed sources of attack traffic. Mathematical model and simulation results demonstrate that the proposed model can reduce the required time for identifying malicious users and locating host machines used as the actual sources of attack packets. Copyright © 2016 John Wiley & Sons, Ltd.