This study aims to investigate the perceptions of near-field communication (NFC) usage for mobile payments in Saudi Arabia. In order to develop a mathematical framework for the acceptance of NFC quality of information for mobile payments, researchers have combined the technological acceptance model (TAM) and the idea of perceived risk. An online and physical study of 1217 NFC portable credit card holders in Saudi Arabia was conducted. Exploratory and confirmatory analyses were utilized to analyze the factor structure of the measurement items, and Smart PLS 2.0 from structural equation modeling (SEM) was used to assess the theories and hypotheses that had been put forth. The results show that (1) social influence, perceived element of risk, and subjective norms each have a negative influence on preconceptions of trust in online payment methods using NFC; (2) social influence, perceived element of risk, and social norms all have a positive effect on satisfaction with the security of electronic payment using NFC; (3) perceived ease of use has a negative effect on perceived confidence in digital payment using NFC; and (4) perceived ease of use has a negative effect on perceived trust in online payment using NFC. As a consequence of these findings, users’ attitudes regarding the use of NFC and behavioral intentions to utilize NFC mobile payment can be revealed. This study created a unique approach for assessing perceptions, perceived trust, and NFC information quality in mobile payment uptake in Saudi Arabia. As a consequence, banks may find this research useful as they implement new strategies to attract more customers, such as perceived security, brand trust, and NFC information quality in mobile payment adaption.

The increasing sophistication of cyber threats necessitates the development of advanced security mechanisms to protect modern networks. Among these threats, spoofing attacks pose a significant risk by enabling malicious actors to impersonate legitimate entities. To address this challenge, we propose a novel Transformer-based deep learning framework designed for the effective detection of spoofing attacks. The core of our novel model is a Transformer neural network, enhanced with a custom attention mechanism to improve feature extraction and classification accuracy. To enhance model interpretability and foster trust in AI-driven security systems, we integrate Explainable AI (XAI) techniques, specifically SHAP analysis, allowing for a deeper understanding of feature contributions in decision-making. The proposed model utilized the CIC IoMT2024 dataset, a benchmark with limited prior research on spoofing attack detection. Further, our approach incorporates comprehensive data preprocessing techniques and employs over-sampling using the synthetic minority oversampling technique (smote) and cleaning using (tomek) these techniques are integrated into links smotetomek to mitigate class imbalance, ensuring a more representative training dataset. The proposed framework is evaluated using benchmark dataset datasets, demonstrating high binary classification performance in spoofing attacks through key metrics such as accuracy, confusion matrix analysis, and other classification benchmarks. The proposed model archived an exact result with Accuracy 99.71%. The findings highlight the potential of Transformer-based architectures in cybersecurity applications, paving the way for real-time threat detection and adaptive defense mechanisms.