ABSTRACT. The advent and widespread acceptance of electronic learning have made most learning environments completely reliant on interconnected devices, which has resulted in smart classrooms and remote monitoring systems. This technological advancement in education is accompanied by significant cybersecurity threats due to the vulnerabilities of unprotected, interconnected, and heterogeneous devices. One among many is the Distributed Denial of Service (DDoS) attacks that often disrupt online classes, data access, and other educational services by flooding educational networks with illegitimate traffic. Signature-based DDoS detection mechanisms have proven inadequate in the IoT environment due to the dynamic nature of modern attacks and resource limitations. To address this challenge, this study proposed a hybrid deep learning model that combined Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and attention mechanisms embedded with incremental learning to enhance DDoS detection and prevention in EIoT networks. The proposed model uses the CNN layers to analyze and extract spatial features from network traffic temporal patterns through LSTM units. while the attention mechanisms gathered information on traffic bahaviour. The incremental component uses geometric metrices to enables the model capture to previously unknown attack patterns. Experimental evaluation using the CIC-IoT2023 and BoT-IoT datasets showed that the model achieved high detection accuracy of 99% for both known and emerging attacks within EIoT scenarios using real-time traffic filtering and SDN-based mitigation as prevention strategies. The model showed high adaptability, reduced false positives, and advanced AI-hybridised capabilities for DDoS detection and prevention in EIoT environments. This shows the effectiveness of the proposed model for detecting and preventing DDoS attacks against EIoT infrastructures.