<![CDATA[This study proposes a lightweight convolutional neural network (CNN) model for anomaly detection in school computer laboratories, aiming to enhance operational reliability and cybersecurity awareness. Real-time event logs were collected from 20 computers (PC01–PC20) at Santo Nicholas School with slight variations in CPU, RAM, and network behavior to simulate real-world heterogeneity. After preprocessing and normalization, the merged dataset contained over 10,000 log entries labeled as normal or anomalous. The proposed lightweight CNN achieved 92.23% F1-score, 91.80% accuracy, and a false positive rate (FPR) of 18.47%, demonstrating a balance between detection precision and computational efficiency. Comparative evaluation shows that this architecture performs competitively while requiring fewer parameters and lower inference latency than conventional CNNs. The results highlight the suitability of the proposed model for deployment in low-resource educational environments, supporting early anomaly detection and preventive maintenance. Future research will explore cross-domain generalization and lightweight deployment through edge-AI integration.]]>
Copyrights © 2025
