Cyber Security Challenges in Student Digital Learning Platforms

Teaching and learning have been revolutionized by digital learning platforms, which offer unparalleled flexibility and access to a wealth of resources. However, cybersecurity threats are increasing as these platforms become more crucial in academic settings. Student data, assessment integrity, and online interactions are the primary targets of hackers. Real-time, complex attacks need a more intelligent and adaptable security strategy than current solutions. Traditional rule-based intrusion detection systems and rudimentary encryption fail to defend digital learning platforms against emerging and dynamic threats effectively. These systems can't identify AI-driven attack strategies or subtle system behavior changes, as learning environments are complex. A robust and adaptable cybersecurity system that can learn from current operations, identify complex attacks in real-time, and respond to emerging weaknesses is urgently needed. The CyberSecure-Learn Framework (CSL-F) utilizes a Transformer-based Deep Learning Model for Real-Time Behavioral Anomaly Detection to overcome these issues. This approach utilizes the self-attention mechanism of transformers to analyze sequential student interaction data for identifying cyber threat trends. Instead of relying on handcrafted features, the Transformer-based model learns from raw data to identify unique and complex threats, including unauthorized access and online test cheating. Anomaly detection and Quantum-Resistant AES-256 Encryption safeguard sensitive student data from traditional and quantum computing-based decryption efforts in the CSL-F. In real-time anomaly detection, CSL-F's transformer-based deep learning model achieves 98.9% accuracy, surpassing established approaches in both speed and precision. AES-256 Quantum-Resistant Encryption safeguarded student data 99% under simulated quantum-based decryption attempts. Even though the Transformer model is complex, the system balanced security and performance with just a 4% latency increase, minimizing the effect on real-time instructional operations.