This study investigates the relationship between Artificial Intelligence (AI) adoption, data policies and regulations, trust, and their combined impact on educational sustainability in the United Arab Emirates (UAE). Through a comprehensive analysis, our findings reveal a significant and robust correlation between AI adoption and educational sustainability, emphasizing the transformative potential of AI technologies in enhancing educational resources and services. However, our study also highlights the need for policymakers to refine regulatory frameworks to bridge the gap between technological advancements and compliance, given the insignificant relationship between data policies and regulations and educational sustainability. Trust emerges as a critical factor, particularly in data privacy and security, underlining the importance of transparency and robust security measures in educational institutions. Additionally, we explore the moderating effects of policies and regulations on AI adoption and trust, suggesting the value of calibrated regulatory environments that encourage responsible AI integration while safeguarding privacy and security. These insights have practical implications for institutions and policymakers, guiding them in leveraging AI for sustainable education while ensuring ethical and secure data practices. 

AI Adoption and Educational Sustainability in Higher Education in the UAE

Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model

O Qualys Web Application Firewall (WAF) é um serviço em nuvem de última geração que proporciona uma combinação incomparável de escalabilidade e simplicidade para a segurança de aplicativos web. Sua abordagem automatizada e adaptável permite bloquear ataques de modo rápido e mais eficiente a vulnerabilidades no servidor na Web, impedir a divulgação de informações confidenciais e controlar onde e quando seus aplicativos são acessados.

Web application firewall

Dynamic Web Application Firewall detection supported by Cyber Mimic Defense approach

As the number of cyber‐attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber‐security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network‐based cyber‐attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber‐attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber‐crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.

Advanced security testing using a cyber‐attack forecasting model: A case study of financial institutions

Signature-based and Machine-Learning-based Web Application Firewalls: A Short Survey

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Signature-based and Machine-Learning-based Web Application Firewalls: A Short Survey