Human factors remain the dominant contributor to cybersecurity incidents, yet awareness training produces only moderate and often non-durable behaviour change, and most evaluated programs are either purely digital or evaluated only at the framework level. This study addresses two gaps: the scarcity of empirical and demographically stratified evidence for multi-modal community-facing awareness programs, and the lack of an explicit account of how artificial intelligence (AI) should be integrated into such programs rather than treated as an optional add-on. We evaluate Cyber4Me, a four-stage individual-awareness intervention (community roadshows, structured training, a hackathon, and a physical–digital escape room) that is wrapped in a cross-cutting AI adaptive layer built entirely on structured performance and behaviour data baseline competency tiering, awareness–behaviour gap detection, predictive early-warning, and personalised recommendation, with no reliance on free text. Using a single-group pre–post design with 130 participants in the UK Black Country region and a multi-dimensional Likert instrument, all four competency domains (confidence, familiarity, GDPR knowledge, incident-response preparedness) improved significantly (paired-t, all p<0.001; large within-participant effects, Cohen’s d≥1.0). Improvement was strongly moderated by demographics: older adults gained most in familiarity, undergraduates in confidence, and lower-education participants in regulatory knowledge. The contributions are as follows: transparent and demographically stratified pre–post evidence for a multi-modal awareness program with effect sizes reported; a fitness-for-purpose comparison against contemporary analogs (KnowBe4, Proofpoint, CyberPatriot, iCAT, CAT-RWE, GPT-CSAT, escape-room studies) that treats AI as a first-class design dimension; and an articulated AI integration architecture for the framework, demonstrated offline on the cohort using only structured performance and behaviour data (no free text). In this architecture, a gradient-boosted classifier assigns participants to three baseline competency tiers at 93.1% cross-validated accuracy; these tiers differ sharply in measured improvement (ANOVA F=68.8, p<0.001; Foundational +1.79 vs. Applied +0.30 scale points), an awareness–behaviour gap segment is detected and predicted from intake signals alone (AUC =0.73), and a recommender routes participants to personalised follow-on tracks. As the design is single-group and self-reported, results are reported as evidence of within-participant change associated with the intervention rather than as a causal efficacy estimate, and the AI layer is demonstrated for feasibility rather than being evaluated as a separate trial arm; the scope is explicitly individual security awareness and behaviour, not technical network, IIoT, or cloud security.

The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification algorithms identifies XGBoost as the superior performer, achieving 93.07% accuracy alongside outstanding precision, recall, and F1-scores. Beyond standard classification metrics, our assessment encompasses ROC-AUC, detection latency, and false positive rate, providing a comprehensive assessment of performance. This work contributes to UAV security by providing a robust and reproducible solution for detecting GPS spoofing attacks, supported by a detailed methodology, a comprehensive evaluation including inference-time latency, and a publicly available dataset.

The evolution of vehicular networks has led to the emergence of Next Generation Vehicle Communication Protocols (NextG-VCP) within Cyber-Physical Vehicular Health Monitoring Systems (CP-VHMS). These systems enable real-time diagnostics, predictive maintenance, and intelligent transportation management. However, current vehicular communication frameworks face challenges such as bandwidth limitations, high latency, interference, scalability constraints, and security vulnerabilities.This paper presents a comprehensive taxonomy of NextG-VCP, analyzing six key dimensions: dependability, mobility, intelligence, efficiency, communication, and security. Unlike existing surveys that treat vehicular networking, cybersecurity, and health monitoring as separate domains, this work provides an integrated six-dimensional analytical taxonomy explicitly aligned with CP-VHMS requirements.We systematically compare state-of-the-art vehicular technologies and discuss emerging solutions such as 6G networks, Digital Twin Networks (DTN), AI-driven security frameworks, blockchain-based authentication, and edge computing. Rather than proposing a new standardized communication protocol, this paper offers a structured analytical synthesis and migration-oriented perspective that bridges legacy in-vehicle networks with NextG-VCP ecosystems.The analysis further incorporates standards alignment with IEEE, 3GPP, ETSI, and ISO frameworks to contextualize protocol evolution within functional safety, cybersecurity, and interoperability requirements.Furthermore, we highlight the growing role of aerial and terrestrial vehicular communication, addressing challenges such as spectrum management, interference mitigation, and intelligent traffic coordination. Failure scenarios, degradation mechanisms, interoperability constraints, and standards alignment considerations are also examined to clarify practical deployment implications.Future research directions are outlined, focusing on real-world deployment strategies, hybrid communication models, adaptive security mechanisms, and resilience-aware architectural design. Our findings contribute to the development of a highly efficient, secure, and intelligent vehicular health monitoring system, paving the way for safer and more resilient connected transportation ecosystems.

The increasing integration of connectivity features in modern vehicles has expanded the attack surface for cyber threats, underscoring the need for robust cybersecurity research tools. This paper introduces CyberVehiCare, an innovative and realistic testbed designed to support the analysis and validation of cybersecurity mechanisms for Vehicle-to-Everything (V2X) automotive systems. By combining real components from a commercial vehicle with emulated V2X modules, CyberVehiCare enables comprehensive testing of in-vehicle and external communication vulnerabilities. The platform supports experimentation with cyberattacks, vulnerability assessments, and the development of security countermeasures, contributing to safer and more resilient automotive networks. The testbed’s design, implementation, and validation are discussed, highlighting its potential to advance research.

The rapid growth of data generated by vehicular engine systems has necessitated the development of efficient frameworks for real-time big data processing. Existing solutions such as Apache Hadoop and Apache Spark, have been widely adopted in various domains due to their respective strengths; Hadoop’s distributed storage capabilities and Spark’s in-memory processing efficiency. However, these standalone frameworks present limitations when applied to vehicular systems, particularly in handling real-time analytics, low-latency requirements, and efficient use of network and energy resources. Previous research, including approaches like the one by [1], which combines Spark with Hadoop YARN for remote sensing data, and [2], which investigates parameter tuning for Hadoop and Spark, highlights the difficulties in achieving optimal performance in dynamic environments, particularly when managing varying data loads, noise levels, and non-numeric attributes. This paper introduces a novel Optimized Hybrid Framework that integrates Hadoop’s distributed storage and Spark’s in-memory processing while addressing the limitations of existing hybrid models. Unlike prior approaches, our framework incorporates a dynamic switching mechanism based on business logic, data urgency, and noise levels, providing an adaptive and efficient solution for real-time vehicular big data analytics. Moreover, the framework introduces controlled noise injection strategies and special handling for non-numeric attributes such as decision and driver behavior data, ensuring data integrity in real-world vehicular datasets. Experimental results demonstrate that the proposed Hybrid Framework outperforms both Hadoop and Spark in key performance metrics, achieving an execution time of 0.030 seconds, latency of 1.67e-7 seconds/row, and a network load of 55.01%. These results highlight the framework’s ability to optimize throughput, minimize latency, and reduce energy consumption, making it particularly suitable for predictive maintenance, fault diagnosis, and adaptive vehicular control systems. This study contributes to the field by offering a practical, scalable, and low-latency solution for vehicular big data analytics, addressing the shortcomings of both standalone and hybrid approaches in the automotive manufacturers domain, fleet operators, and researchers for a more effective tool for vehicle health monitoring. The enhanced data processing capabilities facilitate better vehicle maintenance, increase safety, and reduce operational costs, delivering substantial benefits to the broader community through more dependable and efficient transportation solution.

The rapid integration of connected technologies in modern vehicles has significantly enhanced functionality, efficiency, and user experience, but it has simultaneously introduced complex cybersecurity vulnerabilities. Traditional IT-centric security solutions fall short in addressing the dynamic and critical nature of threats to automotive systems. This paper presents Vehicle Penetration Test as a Service (VPTaaS), a conceptual framework designed to proactively identify, assess, and mitigate cybersecurity risks in connected vehicles. VPTaaS integrates AI-driven threat prediction, multimodal vulnerability analysis, real-time cyber-health monitoring, and manufacturer-agnostic penetration testing into a unified service offering. To validate the market demand and assess the feasibility of this approach, a structured survey involving 83 stakeholders - including vehicle owners, insurance representatives, service providers, and industry professionals - was conducted. Results indicate strong stakeholder awareness of vehicular cybersecurity threats (79% - 86%) and a high preference (86% -88%) for proactive cybersecurity assessments. The technical framework is aligned with industry standards such as ISO 21434 and UNECE WP.29 R155 & R156, emphasizing regulatory compliance and realworld applicability. The findings confirm the market readiness and technical viability of VPTaaS, highlighting its potential to enhance vehicle cybersecurity resilience, protect personal data, and support regulatory compliance. Future work will focus on prototype development, real-world pilot studies, and expansion to autonomous and V2X communication systems.

SummaryCellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.

IoT is a groundbreaking technology that enables wireless interoperability between devices without human intervention. It has become an integral part of human life and has made significant advancements in the automotive industry. The combination of vehicular networks and IoT has opened up new possibilities but has also led to challenges such as degraded QoS (Quality of Service), network congestion, packet loss, transmission delay, intrusions, etc. These problems are also apparent in vehicular health monitoring systems. To overcome such challenges, it is necessary to develop an intricate framework that can address them all. TCP/IP is a five-layer-based standardized communication protocol that facilitates communication between devices over the internet. Unique network protocols and functions are authorized to each of the five layers. In this paper, a comprehensive survey is done on all the recent studies on the five communication layer protocols, their functionalities, and practical challenges associated with each of them for an intra- vehicular health monitoring system (IVHMS). Analyzing them, a novel taxonomy is also proposed with visual representation. In the end, this paper proposes a cross-layer-based framework to address communication- related complexities. This cross-layer model aims to integrate all the dynamic capabilities of the five protocol layers (Application, Transport, Network, Data Link & Physical) and resolve issues within each layer by implementing functionalities of other layer protocols. Future research scopes and possibilities of the proposed system are also discussed.

The fifth generation (5G) wireless communication systems development has brought about a paradigm shift using advanced technologies; including softwarization, virtualization, massive MIMO, and ultra-densification, in addition to introducing new frequency bands. However, as societal needs for any form of information grow, it is necessary to satisfy the UN's Sustainable Development Goals (SDGs). Migrations to 6G and beyond systems are envisioned to provide augmented capacity, so massive IoT, with better performance relying on optimization made possible by artificial intelligence, it is absolutely necessary. Non-Terrestrial Networks (NTNs), including satellite systems, High-Altitude Platforms (HAPs), and Unmanned Aerial Vehicles (UAVs), provide the best solutions to connect the unconnected, unserved, and underserved in remote and rural areas. Over the past few decades, Geo Synchronous Orbits (GSO) satellite systems have been deployed to support broadband services, backhauling, Disaster Recovery and Continuity of Operations (DR-COOP), and emergency services. Recently, novel non-GSO satellite systems are attracting significant interest. Within the next few years, several thousands of Low Earth Orbit (LEO) satellites and mega-LEO constellations will provide global internet services, offering user throughput comparable to terrestrial mobile or fixed access networks. This report represents the 2023 Edition of the INGR Satellite Working Group Report, following the previous three editions ([1], [2], [3]). This edition of the INGR Satellite Working Group Report addresses NTN and 6G more in detail, adding further contributions on optical wireless communications, artificial intelligence techniques, seamless handover, security, and recent standardization efforts given the prospected unification of terrestrial and NTN components of 6G.

Predictive maintenance has gained importance across various industries, including the automotive sector. It is very challenging to detect vehicle failures in advance due to the intricate composition of various components and sensors. The vehicle’s reliability is of utmost importance for ensuring the absence of fatalities or malfunctions to foster economic development. This study introduces an innovative method for developing a predictive framework for vehicle engines with faster and higher decision accuracy. The framework is specifically designed to recognize patterns and abnormalities that may suggest prospective engine problems in real-time and allow proactive maintenance. We assessed the performance of the developed vehicular engine health monitoring systems using a deep learning model based on essential measures like root mean square error, root mean square deviation, mean absolute error, accuracy, confusion matrix, and area under the curve. In this case, the deep learning models are developed by following ensemble techniques using the most prominently used machine learning techniques. Significantly, Stacked Model 1 outperformed other stacked models (Models 2 and 3) and achieved an impressive AUC value of 0.9702 with a low root mean square error (RMSE) of 0.3355, a high accuracy rate of 0.9470, and a precision of 0.9486. It happens due to the effective incorporation of different approaches into Stacked Model 1 , which signifies a significant advancement in predicting vehicular engine failures. The model can be used in real-time monitoring systems to continuously monitor the health of vehicular engines and provide early warnings of potential failures, thereby reducing maintenance costs and improving safety.

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.

The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry's growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur. This paper aims to identify, evaluate, and synthesize studies related to proactive self-healing approaches in MEC environments. The authors conducted a systematic literature review (SLR) using four well-known digital libraries (IEEE Xplore, Web of Science, ProQuest, and Scopus) and one academic search engine (Google Scholar). The review retrieved 920 papers, and 116 primary studies were selected for in-depth analysis. The SLR results are categorized into edge resource management methods and self-healing methods and approaches in MEC. The paper highlights the challenges and open issues in MEC, such as offloading task decisions, resource allocation, and security issues, such as infrastructure and cyber attacks. Finally, the paper suggests future work based on the SLR findings.

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.

The fifth generation (5G) Wireless Communication systems development has brought out a paradigm shift using advanced technologies e.g., softwarization, virtualization, Massive MIMO, ultra-densification and introduction of new frequency bands. However, as the societal needs grow, and to satisfy UN's Sustainable Development Goals (SDGs), 6G and beyond systems are envisioned. Non- Terrestrial Networks including satellite systems, Unmanned Aerial Vehicles (UAVs) and High-Altitude Platforms (HAPs) provide the best solutions to connect the unconnected, unserved and underserved in remote and rural areas in particular. Over the past few decades, Geo Synchronous Orbits (GSO) satellite systems have been deployed to support broadband services, backhauling, Disaster Recovery and Continuity of Operations (DR-COOP) and emergency services. Recently, there is a considerable renewed interest in planning and developing non-GSO satellite systems. Within the next few years several thousands of Low Earth Orbit (LEO) satellites and mega LEO constellations will be ready to provide global Internet services. This report is the 2022 Edition of the INGR Satellite Working Group Report, subsequent to the previous two editions [1] [2]. The topics considered in this INGR Satellite WG 2022 Edition of the roadmap are the following taking 6G systems into account: applications and services, reference architectures (both backhaul and direct access), satellite loT, mm Wave use for satellite networks, machine learning and artificial intelligence, edge computing, QoS/QoE, security, network management and standardization. The work on the roadmap will continue towards the next edition of the roadmap addressing new challenges and potential solutions for future networks.

Data processing in real-time brings better business modeling and an intuitive plan of action. Internet of things (IoT), being a source of sensitive data collected and communicated through either public or private networks, requires better security from end to end to uphold integrity, quality, and acceptability of data. Designing an adaptive solution plays a vital role where IoT is deployed for the sensing-as-a-services in the critical infrastructure and near real-time decision making by deploying data analysis in the edge datacenters. Again, securing the system with user's demand and device specifications is a challenging and open research problem. This paper proposed a decision tree based user-centric security approach named DecisionTSec that provides a secure channel for communication in IoT networks, combining edge datacenters in the network edges. Further, the proposed DecisionTSec is validated by experimenting with the real-time testbed for the system performance along with the theoretical security validation.

A far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy for industrial revolution 4.0. With the integration of social networks with the Internet of Things being noticed in different industries to enhance human involvement and increase their productivity, security in such networks is increasingly alarming. Vulnerabilities can be characterized in the form of privacy invasion, leading to hazardous contents, which can be detrimental to social media actors and in turn impact the processes of the overall Social Network-Integrated Industrial Internet of Things (SN-IIoT) ecosystem. Despite this prevalence, the current platforms do not have any significant level of functionality to capture, process, and reveal unhealthy content among the social media actors. To address those challenges by detecting hazardous contents and create a stable social internet environment within IIoT, a statistical learning-enabled trustworthy analytic tool for human behaviors has been developed in this paper. More specifically, this paper proposes a machine learning (ML)-enabled scheme SPY-BOT, which incorporates a hybrid data extraction algorithm to perform post-filtering that arbitrates the users’ behavior polarity. The scheme creates class labels based on the featured keywords from the decision user and classifies suspicious contacts through the aid of ML. The results suggest the potential of the proposed approach to classify the users’ behavior in SN-IIoT.

Cellular networks are anticipated to handle a significant increase in data traffic, as well as a huge number of devices and new use cases in the very near future; therefore, future 5G networks are being built with heterogeneity in mind. The user's device will be able to connect to the network using 5G and other technologies such as LTE, WiMAX, WiFi, Satellite, etc. Satellite Communication (SatCOM), on the other hand, has been proposed as a network of choice to supplement the 5G use cases. In most of 5G's use cases, satellite communication would provide a complimentary service for global coverage, critical communication, broadcast/multicast provision, and multimedia traffic growth. Hence, for these connections to operate effectively, a smooth transmission between 5G and satellite is needed. However, in a heterogeneous wireless network, a major issue is determining how to assign a user to the best Radio Access Technology (RAT). An intelligent hybrid RAT selection algorithm based on machine learning is proposed in this paper. The User Terminal is trained to select the necessary RAT to admit its call using the Actor-Critic Reinforcement Learning Algorithm. Based on the simulation results, the proposed algorithm has the ability to learn in a competent way to successfully manage resource management autonomously and therefore increases the user's quality of service (QoS). The algorithm also showed better expected total reward, lower call blocking probability and higher throughput when compared with a Markov Reward-Based Greedy Algorithm.