Securing Future Learning in Education 5.0: An Ensemble Learning for Secure Smart Education System (SSEduS)

Introduction: Education system is making its debut in the smart and intelligent system at a slow pace but the change seen in the system is an amazing shift. The learning system has developed and contributed to the disciples in many ways in Education using interactive tools and application of artificial intelligence i.e. Education 5.0. Integration of various technologies makes the Education 5.0 as the smart and intelligent education system which makes a more complex environment with unique security challenges. In this paper we attempt to analyse computer science, security and education to make a Secure Smart Education System (SSES) by using ensemble learning to address the security challenges. This paper addresses these challenges, associated risk and provides a basic analysis for cyber security.. Objectives: The objective of this work is to explore the transformative impact of Education 5.0, which represents a significant evolution in the educational landscape through the integration of advanced technologies, including artificial intelligence and interactive tools. As the education system gradually embraces these smart and intelligent frameworks, it encounters a myriad of unique security challenges that arise from the complexity of this new environment. This paper aims to analyse the intersection of computer science, security, and education to develop a Secure Smart Education System (SSES) that effectively addresses these security challenges. Methods: This paper aims to analyse the intersection of computer science, security, and education to develop a Secure Smart Education System (SSES) that effectively addresses these security challenges. By employing ensemble learning techniques, we seek to enhance the robustness of security measures within educational platforms, thereby mitigating associated risks and vulnerabilities. Furthermore, this work provides a foundational analysis of cybersecurity issues pertinent to the educational sector, offering insights into how to create a safer and more secure learning environment in the context of rapidly evolving technological advancements. Results: The proposed model is named the Secure Smart Education System (SSEduS), as it has shown promising results while using publicly available network data. This model consists of a stacked ensemble that uses three classifiers divided into two categories: traditional models and ensemble models for detecting malicious activity. Unlike other conventional models, our model is a perfect combination of base learners and strong learners. The proposed model has demonstrated good results, achieving nearly perfect accuracy of 99.99% and a false positive rate of 0.46%. The results have shown significant improvement over the existing models studied. The accuracy achieved is the best result so far using the stacked ensemble. Conclusions: Our goal was to contribute to the ongoing discourse on the integration of security in smart education systems, ensuring that the benefits of Education 5.0 can be realized without compromising the safety and integrity of educational data and process.