Supervised Algorithms for Failure Prediction in the UPSE LAN network
DOI:
https://doi.org/10.26423/03cdzf27Keywords:
Machine learning, Anomaly detection, Network monitoring, Failure prediction, University networksAbstract
The UPSE LAN is currently managed through reactive monitoring, lacking historical data and early alerts. This study aimed to predict network failures using SNMP/Zabbix telemetry. The CRISP–DM process was applied with 60-minute windows over 7,571 samples (imbalance: 729 failures; 6,842 normal), comparing two approaches: Random Forest (RF) based on statistical window aggregates, and a 1D Convolutional Neural Network (CNN-1D) trained on raw multivariate sequences. The RF achieved 96.21 % accuracy, 97.16 % precision, and 62.56 % recall (cross-validation). The CNN-1D reached 91.93 % ROC-AUC, 94.37 % accuracy, and 69.41 % recall. Results revealed complementary behaviors: RF minimizes false positives (high precision), while CNN-1D enhances fault detection (higher sensitivity). As the main finding, a hybrid strategy—using CNN-1D for early warning and RF for confirmation—is proposed to enable proactive management, reducing response times and improving service availability.
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