Network Intrusion Detection Systems (NIDS) developed using publicly available datasets predominantly focus on enterprise environments, raising concerns about their effectiveness for converged Information Technology (IT) and Operational Technology (OT) in energy infrastructures. This study evaluates the representativeness of five widely used datasets: CIC-IDS2017, SWaT, WADI, Sherlock, and CIC-Modbus2023 against network-detectable MITRE ATT CK techniques extracted from documented energy sector incidents. Using a structured five-step analytical approach, this article successfully developed and performed a gap analysis that identified 94 network observable techniques from an initial pool of 274 ATT CK techniques. Sherlock dataset exhibited the highest mean coverage (0.56), followed closely by CIC-IDS2017 (0.55), while SWaT and WADI recorded the lowest scores (0.38). Combining CIC-IDS2017, Sherlock, and CIC-Modbus2023 achieved an aggregate coverage of 92

Smart healthcare industry is increasingly relying on Internet of Things (IoT) devices to improve patient care and operational efficiency. However, the cryptographic algorithms that enable fundamental security and are widely used in these cyber systems are vulnerable to attacks by emerging quantum computers - known as Quantum Threat. This paper examines the quantum threat to healthcare IoT across the four layers of the IoT architecture: physical, network, perception, and application. It proposes a comprehensive migration framework integrating a phased hybrid approach with crypto-agility to transition healthcare IoT systems to quantum-safe cryptography. This framework prioritises resource-constrained devices, emphasises interoperability, and considers the challenges of vendor readiness and infrastructure upgrades. This paper contributes a detailed, phased migration plan specifically tailored to the unique security needs and resource limitations of IoT-based healthcare systems.

Healthcare systems face persistent and evolving cyber threats, yet how adversarial tactics and techniques have shifted over time has not been systematically characterised using empirical, multi-source data. This paper analyses 1,214 threat records drawn from three authoritative sources: the MITRE ATT CK behavioural framework, the CISA Known Exploited Vulnerabilities catalogue, and the NIST vulnerability database, covering 44 validated healthcare-targeting threat entities from 2017 to 2024. We show that attacker behaviour has shifted measurably toward stealth-oriented tactics: defense evasion remained the dominant tactic throughout the observation period, consistently accounting for 15-20

FinTech’s increasing connectivity, rapid innovation, and reliance on global digital infrastructures present significant cybersecurity challenges. Traditional cybersecurity frameworks often struggle to identify and prioritize sector-specific domain-specific vulnerabilities or adapt to evolving adversary tactics, particularly in highly targeted sectors such as FinTech. To address these gaps, we propose ISADM (Integrated STRIDE-ATT&CK-D3FEND Threat Model), a novel hybrid methodology applied to FinTech security that integrates STRIDE’s asset-centric threat classification with MITRE ATT&CK’s catalog of real-world adversary behaviors and D3FEND’s structured knowledge of countermeasures. ISADM employs a frequency-based scoring mechanism to quantify the prevalence of adversarial Tactics, Techniques, and Procedures (TTPs), enabling a proactive, score-driven risk assessment and prioritization framework. This proactive approach contributes to shifting organizations from reactive defense strategies toward the strategic fortification of critical assets. We validate ISADM through industry-relevant case study analyses, demonstrating how the approach replicates actual attack patterns and strengthens proactive threat modeling, guiding risk prioritization and resource allocation to the most critical vulnerabilities. Overall, ISADM offers a comprehensive hybrid threat modeling methodology that bridges asset-centric and adversary-centric analysis, providing FinTech systems with stronger defenses. The emphasis on real-world validation highlights its practical significance in enhancing the sector’s cybersecurity posture through a frequency-informed, impact-aware prioritization scheme that combines empirical attacker data with contextual risk analysis.

This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and analysed 11 deep learning models using COVID-19 image, text, and speech (ie, cough) data. Our analysis revealed that the MobileNet model achieved the highest accuracy of 99.97% for COVID-19 image data and 93.73% for speech data (i.e., cough). However, the BiGRU model demonstrated superior performance in COVID-19 text classification with an accuracy of 99.89%. The broader implications of this research suggest potential benefits for other domains and disciplines that could leverage deep learning techniques for image, text, and speech analysis.

Image inpainting is a recognized method for restoring the properties of pixels in damaged or incomplete images in computer vision technology. Some recent techniques based on generative adversarial network (GAN) image inpainting have outperformed traditional approaches due to their excellent deep learning capability and adaptability to various image domains. Since residual networks (ResNet) also gained popularity over time due to their property as a generative model, offering better feature representation and compatibility with other architectures, how could we leverage both of these models to result in even greater success in image inpainting? This paper proposes a novel architecture for image inpainting based on GAN and residual networks. Our proposed architecture consists of three models: Transpose Convolution-based GAN, Fast ResNet-Convolutional Neural Network, and Co-Modulation GAN. Transpose Convolution-based GAN is our newly designed architecture. It produces guided and blind image inpainting, and FR-CNN performs the object removal case. Co-Mod GAN acts as a refinement layer because it refines the results from Transpose Convolution-based GAN and FR-CNN. To train and evaluate our proposed architecture on publicly available benchmark datasets: CelebA, Places2, and ImageNet are used. Our approach proves our hypothesis, and our proposed model acquires the highest accuracy of 96.59

This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and analysed 11 deep learning models using COVID-19 image, text, and speech (ie, cough) data. Our analysis revealed that the MobileNet model achieved the highest accuracy of 99.97 (i.e., cough). However, the BiGRU model demonstrated superior performance in COVID-19 text classification with an accuracy of 99.89 implications of this research suggest potential benefits for other domains and disciplines that could leverage deep learning techniques for image, text, and speech analysis.

FinTech’s increasing connectivity, rapid innovation, and reliance on global digital infrastructures present significant cybersecurity challenges. Traditional cybersecurity frameworks often struggle to identify and prioritize sector-specific domain-specific vulnerabilities or adapt to evolving adversary tactics, particularly in highly targeted sectors such as FinTech. To address these gaps, we propose ISADM (Integrated STRIDE-ATT&CK-D3FEND Threat Model), a novel hybrid methodology applied to FinTech security that integrates STRIDE’s asset-centric threat classification with MITRE ATT&CK’s catalog of real-world adversary behaviors and D3FEND’s structured knowledge of countermeasures. ISADM employs a frequency-based scoring mechanism to quantify the prevalence of adversarial Tactics, Techniques, and Procedures (TTPs), enabling a proactive, score-driven risk assessment and prioritization framework. This proactive approach contributes to shifting organizations from reactive defense strategies toward the strategic fortification of critical assets. We validate ISADM through industry-relevant case study analyses, demonstrating how the approach replicates actual attack patterns and strengthens proactive threat modeling, guiding risk prioritization and resource allocation to the most critical vulnerabilities. Overall, ISADM offers a comprehensive hybrid threat modeling methodology that bridges asset-centric and adversary-centric analysis, providing FinTech systems with stronger defenses. The emphasis on real-world validation highlights its practical significance in enhancing the sector’s cybersecurity posture through a frequency-informed, impact-aware prioritization scheme that combines empirical attacker data with contextual risk analysis.

Quantum computing is emerging as a significant threat to information protected by widely used cryptographic systems. Cryptographic methods, once deemed secure for decades, are now at risk of being compromised, posing a massive threat to the security of sensitive data and communications across enterprises worldwide. As a result, there is an urgent need to migrate to quantum-resistant cryptographic systems. This is no simple task. Migrating to a quantum-safe state is a complex process, and many organisations lack the in-house expertise to navigate this transition without guidance. In this paper, we present a comprehensive framework designed to assist enterprises with this migration. Our framework outlines essential steps involved in the cryptographic migration process, and leverages existing organisational inventories. The framework facilitates the efficient identification of cryptographic assets and can be integrated with other enterprise frameworks smoothly. To underscore its practicality and effectiveness, we have incorporated case studies that utilise graph-theoretic techniques to pinpoint and assess cryptographic dependencies. This is useful in prioritising crypto-systems for replacement.

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain examination and advances in AI-based detection methods, building an accurate and efficient model for detecting and categorizing tumors from MRI images remains a challenge. To address this problem, we proposed a deep Convolutional Neural Network (CNN)-based architecture for automatic brain image classification into four classes and a U-Net-based segmentation model. Using six benchmarked datasets, we tested the classification model and trained the segmentation model, enabling side-by-side comparison of the impact of segmentation on tumor classification in brain MRI images. We also evaluated two classification methods based on accuracy, recall, precision, and AUC. Our developed novel deep learning-based model for brain tumor classification and segmentation outperforms existing pre-trained models across all six datasets. The results demonstrate that our classification model achieved the highest accuracy of 98.7% in a merged dataset and 98.8% with the segmentation approach, with the highest classification accuracy reaching 97.7% among the four individual datasets. Thus, this novel framework could be applicable in clinics for the automatic identification and segmentation of brain tumors utilizing MRI scan input images.

Intelligent transportation and autonomous mobility solutions rely on cooperative awareness developed by exchanging proximity and mobility data among road users. To maintain pervasive awareness on roads, all vehicles and vulnerable road users must be identified, either cooperatively, where road users equipped with wireless capabilities of Vehicle-to-Everything (V2X) radios can communicate with one another, or passively, where users without V2X capabilities are detected by means other than V2X communications. This necessitates the establishment of a communications channel among all V2X-enabled road users, regardless of whether their underlying V2X technology is compatible or not. At the same time, for cooperative awareness to realize its full potential, non-V2X-enabled road users must also be communicated with where possible or, leastwise, be identified passively. However, the question is whether current V2X technologies can provide such a welcoming heterogeneous road environment for all parties, including varying V2X-enabled and non-V2X-enabled road users? This paper investigates the roles of a propositional concept named Augmenting V2X Roadside Unit (A-RSU) in enabling heterogeneous vehicular networks to support and benefit from pervasive cooperative awareness. To this end, this paper explores the efficacy of A-RSU in establishing pervasive cooperative awareness and investigates the capabilities of the available communication networks using secondary data. The primary findings suggest that A-RSU is a viable solution for accommodating all types of road users regardless of their V2X capabilities.

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a realtime framework that incorporates the Internet of Things (IoT), Blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the Internet of Things (IoT) and Blockchain technologies facilitates establishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the Blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 seconds after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms.

Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. However, as the number of data grows, dimension reduction becomes an increasingly difficult task when training ML models. Addressing this, our paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding based on clustering results, as well as Principal Component Analysis (PCA) for dimension reduction and is specifically designed for large and imbalanced datasets. This model's performance is carefully evaluated using three cutting-edge benchmark datasets: UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. On the UNSW-NB15 dataset, our trials show that the RF and ET models achieve accuracy rates of 99.59% and 99.95%, respectively. Furthermore, using the CIC-IDS2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS2018. These performance results continuously outperform the state-of-art, indicating significant progress in the field of network intrusion detection. This achievement demonstrates the efficacy of the suggested methodology, which can be used practically to accurately monitor and identify network traffic intrusions, thereby blocking possible threats.

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain.

As a result of quick transformation to digitalization for providing the employees teleworking/home office services with the capabilities to access company resources from outside the company over Internet using remote desktop and virtual private network (VPN) applications and the increase in digital activity during COVID-19 such as the usage of audio/video conferencing applications, many businesses have been victims of cyber attacks. This paper investigates whether there was an increase in the frequency of cyber attacks during COVID-19. It also identifies the motivations for such attacks in light of software/hardware/system vulnerabilities. Following this research, we also categorize vulnerabilities and develop a taxonomy. Such a taxonomy helped to identify the type of attacks on their frequency and their impact. To do that, we developed a research methodology to collect attack and vulnerability information from the selected databases. Using relevant key words, we developed the taxonomy that led us to create insightful information to answer the research questions that are thoroughly analyzed and presented accordingly. This work also recommended a list of mitigation measures that can be considered in the future to prepare the industry for a similar pandemic including establishing and maintaining a Information Security Management System (ISMS) by following relevant standards (ISO/SAE 2700x, BSI-Standards 200-x, SMEs: CISIS12®).

Abstract Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

Time synchronization utilizing the Global Navigation Satellite System (GNSS) is being increasingly investigated for vehicular networks. Due to GNSS signal blockages, the availability and accuracy of GNSS timing solutions in various road settings is a recognized challenge. With the recent improvement of Multi-GNSS technology and the increased capacity of consumer-grade receivers, the application of GNSS in vehicular environments has brightened up. This paper systematically analyzes the required time synchronization of vehicular networks and presents a GNSS-based time synchronization solution. It also experimentally demonstrates the availability and capabilities of GNSS time synchronization using commercial-grade GNSS receivers and off-the-shelf communication devices. Our experiments show that the timing accuracy of an individual vehicular node can be as good as ±2 microseconds, resulting in synchronization accuracy of sub-10 microseconds among nodes. A momentary complete outage of the GNSS time solution due to signal blockage on the road adds clock error, leading to synchronization inaccuracy of up to sub-20 microseconds. This level of inaccuracy still meets the desired requirement for most applications in vehicular communication.

Brain tumors are among the most fatal and devastating diseases, often resulting in significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to devise treatment plans that can extend the lives of affected individuals. Manually identifying and analyzing large volumes of MRI data is both challenging and time-consuming. Consequently, there is a pressing need for a reliable deep learning (DL) model to accurately diagnose brain tumors. In this study, we propose a novel DL approach based on transfer learning to effectively classify brain tumors. Our novel method incorporates extensive pre-processing, transfer learning architecture reconstruction, and fine-tuning. We employ several transfer learning algorithms, including Xception, ResNet50V2, InceptionResNetV2, and DenseNet201. Our experiments used the Figshare MRI brain tumor dataset, comprising 3,064 images, and achieved accuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50V2, InceptionResNetV2, and DenseNet201, respectively. Our findings reveal that ResNet50V2 achieves the highest accuracy rate of 99.68% on the Figshare MRI brain tumor dataset, outperforming existing models. Therefore, our proposed model’s ability to accurately classify brain tumors in a short timeframe can aid neurologists and clinicians in making prompt and precise diagnostic decisions for brain tumor patients.

In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved.By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60\%, F1 score of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms in order to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.