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

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