Ensemble Learning Techniques for Advanced Threat Detection in Complex Data Environments for Smart Education
Virender Dhiman · Advances in educational technologies and instructional design book series · 2025
As educational environments increasingly leverage digital technologies, they become more susceptible to a myriad of cyber threats. This chapter explores the application of ensemble learning techniques for advanced threat detection in complex data environments, particularly within smart education frameworks. Ensemble learning, which combines multiple machine learning models to enhance predictive performance, provides a robust solution for identifying and mitigating cyber threats in real-time. By analyzing diverse datasets from various educational technologies, the chapter illustrates how these techniques can improve the accuracy and efficiency of threat detection systems. Furthermore, it discusses the integration of ensemble methods with emerging technologies such as IoT, big data analytics, and AI to create a comprehensive security framework tailored for smart education. Case studies demonstrating successful implementations highlight the effectiveness of ensemble learning in adapting to the evolving threat landscape.