NEURALGUARD-EDU: NEXT-GENERATION DEEP LEARNING FRAMEWORK FOR CYBER VULNERABILITY MITIGATION IN DIGITAL LEARNING PLATFORMS

S.Ambika; Dr; K. Abirami; Dr.K.Dharmarajan

doi:10.5281/zenodo.21288618

Abstract

The rapid shift toward pervasive student digital learning platforms between 2025 and 2026 has catalyzed an unprecedented escalation in highly sophisticated cyber threats targeting academic infrastructure. Modern Learning Management Systems (LMS) and distributed educational ecosystems are continuously exposed to advanced persistent threats, deepfake-driven identity spoofing, data exfiltration, automated denial-of-service floods, and injection exploits. Traditional signature-based perimeter defenses are increasingly obsolete against these polymorphic, AI-driven attacks. To address these critical vulnerabilities, this paper presents DeepGuardEdu, a novel, multi-layered deep learning framework engineered specifically for digital learning environments. DeepGuard-Edu integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with self-attention mechanisms and Convolutional Neural Networks (CNN) to achieve real-time anomaly detection, user behavioral profiling, and proactive mitigation. By analyzing multimodal telemetry streams—including student access logs, API interaction sequences, and network packet structures—the proposed framework effectively intercepts advanced zero-day exploits, session hijacking, and database breaches. Experimental results demonstrate that DeepGuard-Edu achieves an outstanding detection accuracy of 99.42% across diverse simulated educational logs, outperforming existing legacy machine learning benchmarks while maintaining a remarkably low false-positive rate of 0.04%. Furthermore, we discuss the integration of Explainable AI (XAI) paradigms to provide transparent security insights, ensuring compliant and trustworthy deployment in global institutional frameworks.

Keywords

Cyber Security, Deep Learning, Digital Learning Platforms, BiLSTM, Learning Management Systems (LMS), Intrusion Detection, Data Privacy, 2026 Educational Tech.

References

1) Ajayi, B., Barakat, B., & McGarry, K. (2025). Leveraging VAE-Derived Latent Spaces for Enhanced
Malware Detection with Machine Learning Classifiers. ArXiv Preprint ArXiv:2503.20803.
2) Akgündoğdu, A., & Çelikbaş, Ş. (2025). Explainable deep learning framework for brain tumor
detection: Integrating LIME, Grad-CAM, and SHAP for enhanced accuracy. Medical Engineering &
Physics, 144, 104405.
3) Alier, M., Casañ, M. J., & Guerrero, A. (2024). AI-powered teaching and learning platforms: Evolving
architectures in higher education ecosystems. Computers & Education, 210, 104930.
4) Almajed, H., Alsaqer, A., & Frikha, M. (2025). Imbalance Datasets in Malware Detection: A Review
of Current Solutions and Future Directions. International Journal of Cyber Security and Intelligence,
7(2), 112-129.
5) Atchley, W., Sterling, K., & Zhao, Y. (2024). Chatbots as transformative pedagogical agents: Real-time
instructional feedback loops. Frontiers in Education, 9, 1140-1152.
6) Bobula, M. (2024). Adaptive learning and language support via large language models in digital
classrooms. Journal of Artificial Intelligence in Education, 34(1), 45-68.
7) Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the art.
British Journal of Educational Technology, 54(6), 1420-1438.
8) FNAS Journal. (2026). Deep Learning-Based Malware Detection Under Real-World Constraints: A
Systematic Review of Class Imbalance, Concept Drift, and Interpretability. Vol. 3, No. 1, 2026-FNASJCA-3-1.
9) Frontiers. (2025). Teaching log data analysis in Indian cybersecurity classrooms: a mixed-methods
study of pedagogical challenges and learner difficulties. Frontiers in Education, vol. 10, Article
1676938.
10) Khalid, I. (2025). An Integrated Deep Learning Framework for Real-Time Monitoring of Student
Engagement in Smart Classrooms. ICCK Journal of Image Analysis and Processing, 1(4), 172-183.
11) Khalid, R., & Naqi Raza, M. (2024). A Thorough Examination of the Importance of Machine Learning
and Deep Learning Methodologies in the Realm of Cybersecurity: An Exhaustive Analysis. Journal of
Engineering Research and Sciences, 3(7), 11-22.
12) Labadze, N., Groundstroem, S., & Taylor, L. (2023). Generative AI tool usage among university
students: Opportunities and validation limits. Computers and Education: Artificial Intelligence, 5,
100140.
13) Ogundeji, O. A. (2025). The Future of Science Education Development: Cyber Security and Artificial
Intelligence (AI). SSR Journal of Artificial Intelligence (SSRJAI), 2(6), 1-7. DOI:
10.5281/zenodo.17750884.
14) Thüs, H., Rademacher, M., & Klamma, R. (2024). OwlMentor: Document-based intelligent chat and
automated question generation for smart education. IEEE Transactions on Learning Technologies, 17,
88-101.
15) World Intellectual Property Organization. (2024). Generative AI: Structural transformation, algorithmic
storage, and technical safeguard mandates in national educational frameworks. WIPO Technology
Trends Report, 2024.