Explainable Machine Learning for Performance Anomaly Detection and Classification in Mobile Networks

Abstract

Mobile communication providers continuously collect many parameters, statistics, and key performance indicators (KPIs) with the goal of identifying operation scenarios that can affect the quality of Internet-based services. In this regard, anomaly detection and classification in mobile networks have become challenging tasks due to both the huge number of involved variables and the unknown distributions exhibited by input features. This paper introduces an unsupervised methodology based on both a data-cleaning strategy and explainable machine learning models to detect and classify performance anomalies in mobile networks. Specifically, this methodology dubbed explainable machine learning for anomaly detection and classification (XMLAD) aims at identifying features and operation scenarios characterizing performance anomalies without resorting to parameter tuning. To this end, this approach includes a data cleaning stage that extracts and removes outliers from experiments and features to train the anomaly detection engine with the cleanest possible dataset. Moreover, the methodology considers the differences between discretized values of the target KPI and labels predicted by the anomaly detection engine to build the anomaly classification engine which identifies features and thresholds that could cause performance anomalies. The proposed methodology incorporates two decision tree classifiers to build explainable models of anomaly detection and classification engines whose decision structures recognize features and thresholds describing both normal behaviors and performance anomalies. We evaluate the XMLAD methodology on real datasets captured by operational tests in commercial networks. In addition, we present a testbed that generates synthetic data using a known TCP throughput model to assess the accuracy of the proposed approach.