In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

Advancements in technology have had a profound impact on the way we learn, teach, and access knowledge. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a dynamic, engaging, and inclusive space for education. One of the promising advancements in the field of artificial intelligence technology is ChatGPT which offers personalized and effective learning experiences by providing students with customized feedback and explanation. The effect of ChatGPT must be compared with the effect of Google at the educational level since both present a source of information and explanation. Thus, this study aims at investigating the differences between these two learning sources to measure their effectiveness from different perspectives. The model proposed in this study was evaluated using the PLS-SEM approach, utilizing data collected from 153 university students in the UAE. The results of this evaluation indicate that the GPT (Generative Pre-trained Transformer) has a significant impact on user acceptance, mediated by information quality, system quality, perceived learning value, and perceived satisfaction. These factors play a crucial role in determining users' acceptance of the GPT. However, it is important to note that some aspects of the model were not supported, suggesting that they do not have a significant predictive effect on the use of ChatGPT. Nonetheless, the findings of this study contribute to the existing literature on AI and environmental sustainability, providing valuable insights for practitioners, policymakers, and AI product developers. These insights can help guide the development and implementation of AI technologies in a way that aligns with users' needs and preferences while considering the larger environmental context.

A Distributed Denial of Service (DDoS) attack occurs when an attacker tries to disrupt a network, service or website by flooding huge numbers of packets on the internet traffic. Detecting DDoS attacks serves the goal of spotting and addressing them promptly to reduce their effects on the network, system or service being targeted. Detecting Distributed Denial of Service (DDoS) attacks is crucial, for people, companies and network managers. The detection of DDoS attacks has ranging uses in industries such as network security safeguarding websites, managing cloud services ensuring the security of online systems and services. Detecting DDoS attacks is essential for safeguarding infrastructure upholding service availability and guaranteeing the security of online systems and services. To achieve this objective, we proposed a framework to detect DDoS attacks including six steps. In step one, we start by gathering information, which includes network activity and system records, for operations as well as instances of DDoS attacks. Step two, we identify characteristics of the data collected such as patterns in network traffic, packet details, IP addresses, types of protocols used and more. Step three, we utilize algorithms for feature selection such as Salp Swarm Algorithm (SSA), Gray Wolf Algorithm (GWA), Particle Swarm Algorithm (PSO) to pinpoint the features that can distinguish between normal activities and DDoS attack patterns. After that in step four, we divide the processed dataset into sections for training and testing purposes to develop and assess the machine learning models such as SVM (support vector machine), and KNN (K-nearest neighbor). Step five we develop a classification model using machine learning techniques like decision trees, forests, support vector machines (SVM) logistic regression models or neural networks. Finally, we assess the effectiveness of models through metrics such as accuracy rates, precision levels, recall rates, and F1 scores. The results show that the proposed models achieve high results (99.9%). In summary detecting DDoS attacks is crucial for protecting networks, systems and online services against disruptions.