Domain adaptive deep semi-supervised transfer learning for anomaly detection in OpenWiFi

Evaluating service connectivity of OpenWiFi to endusers calls for meticulous analysis of traffic patterns to identify any unusual behavior. This work proposes an automated approach to annotate the partially labeled OpenWiFi data more effectively, enabling discrimination of anomalous behavior from normal traffic behavior. Our framework comprises feature selection, semi-supervised learning (SSL), deep semi-supervised transfer learning (DSSTL), and K-Means clustering analysis for forecasting pseudo labels on unlabeled data, utilizing limited labeled information to extract relevant Key Performance Indicators (KPIs). We utilized one-dimensional convolutional neural network (1D-CNN) and Long Short-Term Memory (LSTM), enhanced by semi-supervised learning (SSL) and unsupervised K-Means to generate pseudo labels as anomaly or normal for unlabeled data. DSSTL involves domain adaptation by combining SSL, transfer learning, and K-Means clustering algorithm, thereby generalizing the forecasting quality of pseudo labels over conventional SSL and clustering approach. Our findings showcase DSSTL-based 1D-CNN with Mean Square Error (MSE) loss outperforms the analysis for SSL and DSSTL-based LSTM in acquiring pseudo labels for all proportions of unlabeled data, as indicated by Calinski and Harabasz (CH) score. It is noticeable that DSSTL-based 1D-CNN with MSE achieves a CH score that increases by 94% corresponding to 25% over 30% unlabeled samples, regardless of the increasing trend in CH score with an increase in proportions of unlabeled samples.