Mahmoud Elkhodr

Mahmoud Elkhodr, Ergun Gide

The rapid advancement of Generative Artificial Intelligence (GenAI) has introduced new opportunities for transforming higher education, particularly in fields that require analytical reasoning and regulatory compliance, such as cybersecurity management. This study presents a structured framework for integrating GenAI tools into cybersecurity education, demonstrating their role in fostering critical thinking, real-world problem-solving, and regulatory awareness. The implementation strategy followed a two-stage approach, embedding GenAI within tutorial exercises and assessment tasks. Tutorials enabled students to generate, critique, and refine AI-assisted cybersecurity policies, while assessments required them to apply AI-generated outputs to real-world scenarios, ensuring alignment with industry standards and regulatory requirements. Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application. Student reflections and instructor observations revealed improvements in analytical engagement, yet challenges emerged regarding AI over-reliance, variability in AI literacy, and the contextual limitations of AI-generated content. Through structured intervention and research-driven refinement, students were able to recognize AI strengths as a generative tool while acknowledging its need for human oversight. This study further highlights the broader implications of AI adoption in cybersecurity education, emphasizing the necessity of balancing automation with expert judgment to cultivate industry-ready professionals. Future research should explore the long-term impact of AI-driven learning on cybersecurity competency, as well as the potential for adaptive AI-assisted assessments to further personalize and enhance educational outcomes.

Mahmoud Elkhodr, Zuhaib Bari Mufti

The IoT is described as a smart interactive environment where devices communicate together ubiquitously sometimes in the background, performing functions on behalf of the users and offering many advanced services to them. Examples range from simple smart home applications such as ambient intelligence and remote controlling functionalities to more advanced smart cities setups. A smart IoT city for instance will encompass a network of many interconnected networks where various sensors and actuators distributed across many areas of the city share information, create knowledge and trigger actuation events. In such a dynamic and rich environment, it is vital for security to trace the source of data and verify its origin. This where data provenance in the IoT come to play. This work attempts to explore requirements and applications of data provenance in the IoT and the challenges pertaining to its realisation.

Mahmoud Elkhodr, Seyed Shahrestani, Hon Cheung

The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.