A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults

TL;DR

The paper presents SEMC-AD, a supervised deep learning-based method for anomaly detection in mobile network alarm logs, outperforming traditional classifiers and reducing manual monitoring efforts.

Contribution

Introduces SEMC-AD, a novel supervised embedding and clustering approach that effectively detects network faults with high accuracy, especially in imbalanced datasets with categorical features.

Findings

Achieves 99% anomaly detection accuracy.

Outperforms random forest and XGBoost in detection performance.

Effectively clusters anomalies using principal components and Gaussian clustering.

Abstract

The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD), a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring caused by the growing volume of alarm logs. SEMC-AD employs a supervised embedding approach based on deep neural networks, utilizing historical alarm logs and their labels to extract numerical representations for each log, effectively addressing the issue of imbalanced classification due to a small proportion of anomalies in the dataset without employing one-hot encoding. The robustness of the embedding is evaluated by plotting the two most significant principle components of the embedded alarm logs, revealing that anomalies form distinct clusters with similar embeddings. Multivariate normal Gaussian clustering is then applied to these components, identifying clusters with a high ratio of anomalies to normal alarms (above 90%) and labeling them as the anomaly group. To classify new alarm logs, we check if their embedded vectors' two most significant principle components fall within the anomaly-labeled clusters. If so, the log is classified as an anomaly. Performance evaluation demonstrates that SEMC-AD outperforms conventional random forest and gradient boosting methods without embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and XGBoost only detect 86% and 81% of anomalies, respectively. While supervised classification methods may excel in labeled datasets, the results demonstrate that SEMC-AD is more efficient in classifying anomalies in datasets with numerous categorical features, significantly enhancing anomaly detection, reducing operator burden, and improving network maintenance.