The technology acceptance model is a widely used model to investigate whether users will accept or refuse a new technology. The Metaverse is a 3D world based on virtual reality simulation to express real life. It can be considered the next generation of using the internet. In this paper, we are going to investigate variables that may affect users’ acceptance of Metaverse technology and the relationships between those variables by applying the extended technology acceptance model to investigate many factors (namely self-efficiency, social norm, perceived curiosity, perceived pleasure, and price). The goal of understanding these factors is to know how Metaverse developers might enhance this technology to meet users’ expectations and let the users interact with this technology better. To this end, a sample of 302 educated participants of different ages was chosen to answer an online Likert scale survey ranging from 1 (strongly disagree) to 5 (strongly agree). The study found that, first, self-efficiency, perceived curiosity, and perceived pleasure positively influence perceived ease of use. Secondly, social norms, perceived pleasure, and perceived ease of use positively influences perceived usefulness. Third, perceived ease of use and perceived usefulness positively influence attitude towards Metaverse technology use, which overall will influence behavioral intention. Fourth, the relationship between price and behavioral intention was significant and negative. Finally, the study found that participants with an age of less than 20 years were the most positively accepting of Metaverse technology.

Securing online financial transactions has become a critical concern in an era where financial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cybercriminals. This study proposed a framework that combines multi-factor authentication and machine learning to increase the safety of online financial transactions. Our methodology is based on using two layers of security. The first layer incorporates two factors to authenticate users. The second layer utilizes a machine learning component, which is triggered when the system detects a potential fraud. This machine learning layer employs facial recognition as a decisive authentication factor for further protection. To build the machine learning model, four supervised classifiers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classifier was 97.938%, 97.881%, 96.717%, and 92.354%, respectively. This study’s superiority is due to its methodology, which integrates machine learning as an embedded layer in a multi-factor authentication framework to address usability, efficacy, and the dynamic nature of various e-commerce platform features. With the evolving financial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work.

The increasing popularity of hologram projection fans has brought about new opportunities for advertising, entertainment, and communication. However, as with any new technology, there is a potential for misuse and criminal activity. The use of digital forensic analysis techniques to investigate hologram projection fan incidents is an important area of research that can aid law enforcement and security agencies in identifying and prosecuting individuals involved in illegal activities. Therefore, this study aims to perform a digital forensic analysis of hologram projection fans, in order to provide a better understanding of how these devices can be used in criminal activity and to develop effective techniques for investigating such incidents. To this end, novel experiments were conducted to simulate the illegal usage intent of the holographic fans, then we collect and analyze experimental data to retrieve digital forensics artifacts that have not been discovered before. The results show that: first, the removable SD card of the hologram fan contains all multimedia that were run on the fan. Second, it's essential to perform a physical image when acquiring data, because individuals may take actions when using the fan to display illicit content such as attempting to hide their tracks by deleting evidence from the Secure Digital (SD) card or other devices. In future work, other devices with different operating systems may be involved to highlight the obstacles that face the process of successful forensic analysis.

Remote Access Trojans (RATs) pose a serious cybersecurity risk due to their stealthy control over compromised systems. This study presents a detection framework that integrates host, network, and newly engineered behavioral features to enhance the identification of RATs. Two sets of experiments were performed: (i) using the original dataset only, and (ii) using an extended dataset with ten engineered features and importance analysis. The framework was evaluated on a public Kaggle dataset of an RAT and benign traffic. Eight machine learning classifiers were tested, including three baseline methods, four ensemble approaches, and a neural network. Results show that the engineered hybrid feature set substantially improves detection performance. Among the tested algorithms, Random Forest and MLP achieved the strongest performance, with accuracies of 98% and 97%, respectively, while Gradient Boosting and LightGBM also performed competitively. Performance was assessed using multiple metrics, and to gain deeper insight into model learning behavior, learning curves and Precision–Recall curves were analyzed. The results demonstrate how well hybrid feature modeling, neural networks, and ensemble machine learning techniques may improve RAT identification. In future work, exploring the use of explainable ML methods may improve the detection capabilities.

Decentralized Federated Learning has surfaced as a promising framework for privacy-aware cooperative learning across distributed nodes without central coordination. However, the decentralized structure introduces complex privacy and scalability challenges. This paper introduces a structured review of privacy-preserving techniques within DFL, focusing on Differential Privacy, Homomorphic Encryption, and Secure Multi-party Computation. These techniques are assessed in terms of their integration into DFL frameworks, with attention to trade-offs in computation, communication, and deployment contexts. A comparative analysis across key dimensions is conducted to highlight technique-specific strengths and limitations. Rather than favoring a single solution, this review underscores the need for context-aware or hybrid approaches tailored to application requirements. Emerging directions are also identified to guide future developments in privacy-preserving DFL.