Time-Lagged Backpropagation Neural Network-Based Machine Learning for Loop Location Identification in Layer 2 Switch Network

Abstract

This research aimed to study and analyze loop problems in Layer 2 switching networks and their impacts on system performance, to develop a machine learning model using a Time-Lagged Backpropagation Neural Network to identify the locations of network loop problems, and to evaluate and compare the performance of the developed model based on defined metrics. The research methodology consisted of eight main steps: 1) network simulation, 2) loop problem simulation, 3) traffic data collection, 4) data preparation and cleaning, 5) training and testing data splitting, 6) model development using various algorithms, 7) parameter tuning, and 8) model performance evaluation using a confusion matrix in conjunction with statistical metrics. The results indicated all five developed models successfully identified the locations of loop occurrences. Among these, the Time-Lagged Backpropagation Neural Network delivered the best performance, achieving an Accuracy of 91.46%, Precision of 95.27%, Recall of 91.46%, an F1-score of 93.11%, and an Area Under the Curve (AUC) of 0.986. These findings demonstrated that the proposed model possessed high efficiency in accurately detecting and locating network loop problems.