Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets

Abstract

As the demand for mobile network services increases, immediate detection and forecasting of network failure events have become important problems for service providers. Several event detection approaches have been proposed to tackle these problems by utilizing social data. However, these approaches have not tried to solve event detection and forecasting problems from multiple data sets, such as web access logs and search queries. In this paper, we propose a machine learning approach that incorporates multiple user activity data into detecting and forecasting failure events. Our approach is based on a two-level procedure. First, we introduce a novel feature construction method that treats both the imbalanced label problem and the data sparsity problem of user activity data. Second, we propose a model ensemble method that combines outputs of supervised and unsupervised learning models for each data set and gives accurate predictions of network service outage. We demonstrate the effectiveness of the proposed models by extensive experiments with real-world failure events occurred at a network service provider in Japan and three user activity data sets.

A survey on artificial intelligence techniques for security event correlation: models, challenges, and opportunities

“…Model ensembles combine several different models of learning, including both supervised and unsupervised, or shallow and deep. The approach by Oki et al (2018) uses an ensemble of RF, logistic regression (LR) and AE models to detect and predict mobile network outages using multiple sets of user activity data. Ghafouri et al (2018) describe an ensemble predictor that contains a deep neural network (DNN) and a linear regression model to detect anomalous cyber-physical sensor readings, where each sensor's measurement is predicted as a function of other sensors.…”

“…• Intrusion detection (ID), which deals with multi-stage and targeted attacks (Joloudari et al, 2020;Sen et al, 2022), or anomaly detection (AD) (Han et al, 2020;Wang et al, 2022) to notify the security administrator about misuses and deviations from normal behavior, respectively. • Intrusion prediction (IP) (Holgado et al, 2017;Oki et al, 2018) based on incoming events, which allows early detection of intruder targets.…”

“…A suitable solution is to study the attacker's behavior given the semantics and context of security events. Profiling of different types of attackers is also an important and promising area (Oki et al, 2018). • Explainability of event semantics.…”