Abstract

With the broad use of e-learning platforms in institutions of higher learning and corporate training, the cyber-attack surface has grown considerably, placing the digital learning ecosystem at risk of account takeover, credential stuffing, insider abuse, automated bot attacks, and examination fraud. The conventional rule-based and signature-based security systems cannot identify dynamic and context-sensitive attacks. In this paper, the author presents a recommendation of an Adaptive Security Model of an e-learning platform that combines both Multi-Layered Anomaly Detection and Context-Aware Risk Assessment to enhance detection accuracy and reduce cases of false alarms. The framework integrates behaviour profiling, network traffic analytics, device fingerprinting, and session-based anomaly scoring in a hierarchical structure. A hybrid engine is used to identify known and unknown attacks, combining a Random Forest by using LSTM-based sequential modeling and an Isolation Forest. Dynamic risk scoring takes into consideration contextual parameters such as a deviation of the location of logins, temporal irregularity, frequency of access, patterns of device change, and anomalies of course interaction. Experimental evaluation conducted on a dataset comprising 135,687 user sessions demonstrates that the proposed model achieves 96.8% detection accuracy, 95.4% precision, and 94.9% recall with a 2.7% false positive rate, outperforming single-layer detection systems by 11.3% in F1-score and achieving an AUC of 0.979 and MCC of 0.944. The strength of the improvements in the case of various attack scenarios is statistically tested (p < 0.01). The findings confirm the fact that implementing multi-layer anomaly detection in combination with adaptive context-driven risk assessment can play a critical role in improving security posture without affecting user experience, which offers an intelligent and scalable architecture to present-day e-learning platforms.