In recent years, Cyber Security threat modeling has been discovered to have the capacity of combatting and mitigating against online threats. In order to minimize the associated risk, these threats need to be modelled with appropriate Intelligent User Interface (IUI) design and consequently the development and evaluation of threat metrics. Artificial Intelligence (AI) has revolutionized every facet of our daily lives and building a responsive Cyber Security Threat Model requires an IUI. The current threat models lack IUI, hence they cannot deliver convenience and efficiency. However, as the User Interface (UI) functionalities and User Experience (UX) continue to increase and deliver more astonishing possibilities, the present threat models lack the predictability capacity thus Machine Learning paradigms must be incorporated. Meanwhile, this deficiency can only be handled through AI-enabled UI that utilizes baseline principles in the design of interfaces for effective Human-Machine Interaction (HMI) with lasting UX. IUI helps developers or designers enhance flexibility, usability, and the relevance of the interaction to improving communication between computer and human. Baseline principles must be applied for developing threat models that will ensure fascinating UI-UX. Application of AI in UI design for Cyber Security Threat Modeling brings about reduction in critical design time and ensures the development of better threat modeling applications and solutions.

Advancements in information technology have significantly enhanced productivity and efficiency through the adoption of cloud computing, yet this adoption has also introduced a spectrum of security threats. Effective cybersecurity mitigation strategies are imperative to minimize the impact on cloud infrastructure and ensure reliability. This study seeks to categorize and assess the risk levels of cybersecurity threats in cloud computing environments, providing a comprehensive characterization based on eleven major causes, including natural disasters, loss of encryption keys, unauthorized login access, and others. Using fuzzy set theory to analyze uncertainties and model threats, threats were identified, prioritized, and categorized according to their impact on cloud infrastructure. A high level of data loss was revealed in five key features, such as encryption key compromise and unauthorized login access, while a lower impact was observed in unknown cloud storage and exposure to sensitive data. Seven threat features, including encryption key loss and operating system failure, were found to significantly contribute to data breaches. In contrast, others like virtual machine sharing and impersonation, exhibited lower risk levels. A comparative analysis of threat mitigation techniques determined Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service and Elevation of Privilege (STRIDE) as the most effective methodology with a score of 59, followed by Quality Threat Modeling Methodology (QTMM) (57), Common Vulnerability Scoring System (CVSS) (51), Process for Attack Simulation and Threat Analysis (PASTA) (50), and Persona non-Grata (PnG) (47). Attack Tree and Hierarchical Threat Modeling Methodology (HTMM) each achieved 46, while Linkability, Identifiablility, Nonrepudiation, Detectability, Disclosure of Information, Unawareness and Noncompliance (LINDDUN) scored 45. These findings underscore the value of fuzzy set theory in tandem with threat modeling to categorize and assess cybersecurity risks in cloud computing. STRIDE is recommended as an effective modeling technique for cloud environments. This comprehensive analysis provides critical insights for organizations and security experts, empowering them to proactively address recurring threats and minimize disruptions to daily operations.

A contrived dataset of substation automation for cybersecurity research in the smart grid networks based on IEC61850

Handwritten digit classification represents a foundational task in computer vision and has been widely adopted in applications ranging from Optical Character Recognition (OCR) to biometric authentication. Despite the availability of large benchmark datasets, the development of models that achieve both high accuracy and computational efficiency remains a central challenge. In this study, the performance of three representative machine learning paradigms—Chi-Squared Automatic Interaction Detection (CHAID), Generative Adversarial Networks (GANs), and Feedforward Deep Neural Networks (FFDNNs)—was systematically evaluated on the Modified National Institute of Standards and Technology (MNIST) dataset. The assessment was conducted with a focus on classification accuracy, computational efficiency, and interpretability. Experimental results demonstrated that deep learning approaches substantially outperformed traditional Decision Tree (DT) methods. GANs and FFDNNs achieved classification accuracies of approximately 97%, indicating strong robustness and generalization capability for handwritten digit recognition tasks. In contrast, CHAID achieved only 29.61% accuracy, highlighting the limited suitability of DT models for high-dimensional image data. It was further observed that, despite the computational demand of adversarial training, GANs required less time per epoch than FFDNNs when executed on modern GPU architectures, thereby underscoring their potential scalability. These findings reinforce the importance of model selection in practical deployment, particularly where accuracy, computational efficiency, and interpretability must be jointly considered. The study contributes to the ongoing discourse on the role of artificial intelligence (AI) in pattern recognition by providing a comparative analysis of classical machine learning and deep learning approaches, thereby offering guidance for the development of reliable and efficient digit recognition systems suitable for real-world applications.