Design and Application of National Security Education Network Platform Based Deep Optimized Learning Framework

In the modern digital infrastructure, national security education has become more prominent in creating Cybersecurity awareness, information protection and defense systems. To implement it in an effective way modernised teaching approaches are badly required. However, the existing online education platforms often face several challenges including lack of intelligent content recommendation, weak adaptability to diverse learning needs, and limited capabilities in detecting fraudulent activities or malicious content that may compromise learning integrity. These limitations hinder the effectiveness of national security education, where reliability, personalization, and security are of utmost importance. To address these issues, this research proposes hybrid deep learning model combining Generative Adversarial Networks (GANs) and CAT optimized Bi-LSTMs for a design and application of a national security education network platform. GAN component is utilized to generate high-quality synthetic learning content and security scenarios that enhance training diversity, while the Bi-LSTM model is employed for robust content classification, learner behaviour analysis, and anomaly detection within the platform. For implementation and validation, datasets such as NSL-KDD Dataset are adopted and measures like accuracy, precision, recall and F1-score are evaluated. Experimental outcomes demonstrate that Optimized model significantly improves detection accuracy of 99.2 %, enhances adaptability in content personalization, and ensures a secure learning environment compared to traditional methods.