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Fault Detection in Mobile Networks Using Diffusion Models

Mohamad Nabeel, Doumitrou Daniil Nimara, Tahar Zanouda

TL;DR

This work addresses fault detection in mobile telecom networks by applying diffusion-based time-series anomaly detection to multivariate data from RAN nodes. It introduces a diffusion framework and a specialized SSSDS4 architecture to learn from operational TS data in an unsupervised manner, followed by a per-feature Z-score post-processing step to flag anomalies. On a real-world, private Ericsson dataset, the reconstruction-based diffusion approach achieves the strongest F1 score and favorable precision, while forecasting-based diffusion and competing baselines show varying trade-offs between precision and recall, highlighting complementarities. The results suggest diffusion models can enhance fault detection speed and reliability in software-intensive telecom environments, with future work addressing non-Gaussian data, multivariate/contextual anomalies, and spatio-temporal diffusion for distributed networks.

Abstract

In today's hyper-connected world, ensuring the reliability of telecom networks becomes increasingly crucial. Telecom networks encompass numerous underlying and intertwined software and hardware components, each providing different functionalities. To ensure the stability of telecom networks, telecom software, and hardware vendors developed several methods to detect any aberrant behavior in telecom networks and enable instant feedback and alerts. These approaches, although powerful, struggle to generalize due to the unsteady nature of the software-intensive embedded system and the complexity and diversity of multi-standard mobile networks. In this paper, we present a system to detect anomalies in telecom networks using a generative AI model. We evaluate several strategies using diffusion models to train the model for anomaly detection using multivariate time-series data. The contributions of this paper are threefold: (i) A proposal of a framework for utilizing diffusion models for time-series anomaly detection in telecom networks, (ii) A proposal of a particular Diffusion model architecture that outperforms other state-of-the-art techniques, (iii) Experiments on a real-world dataset to demonstrate that our model effectively provides explainable results, exposing some of its limitations and suggesting future research avenues to enhance its capabilities further.

Fault Detection in Mobile Networks Using Diffusion Models

TL;DR

This work addresses fault detection in mobile telecom networks by applying diffusion-based time-series anomaly detection to multivariate data from RAN nodes. It introduces a diffusion framework and a specialized SSSDS4 architecture to learn from operational TS data in an unsupervised manner, followed by a per-feature Z-score post-processing step to flag anomalies. On a real-world, private Ericsson dataset, the reconstruction-based diffusion approach achieves the strongest F1 score and favorable precision, while forecasting-based diffusion and competing baselines show varying trade-offs between precision and recall, highlighting complementarities. The results suggest diffusion models can enhance fault detection speed and reliability in software-intensive telecom environments, with future work addressing non-Gaussian data, multivariate/contextual anomalies, and spatio-temporal diffusion for distributed networks.

Abstract

In today's hyper-connected world, ensuring the reliability of telecom networks becomes increasingly crucial. Telecom networks encompass numerous underlying and intertwined software and hardware components, each providing different functionalities. To ensure the stability of telecom networks, telecom software, and hardware vendors developed several methods to detect any aberrant behavior in telecom networks and enable instant feedback and alerts. These approaches, although powerful, struggle to generalize due to the unsteady nature of the software-intensive embedded system and the complexity and diversity of multi-standard mobile networks. In this paper, we present a system to detect anomalies in telecom networks using a generative AI model. We evaluate several strategies using diffusion models to train the model for anomaly detection using multivariate time-series data. The contributions of this paper are threefold: (i) A proposal of a framework for utilizing diffusion models for time-series anomaly detection in telecom networks, (ii) A proposal of a particular Diffusion model architecture that outperforms other state-of-the-art techniques, (iii) Experiments on a real-world dataset to demonstrate that our model effectively provides explainable results, exposing some of its limitations and suggesting future research avenues to enhance its capabilities further.
Paper Structure (16 sections, 9 equations, 4 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 9 equations, 4 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Forecasting-based anomaly detection
  4. Reconstruction-based Anomaly Detection
  5. Data
  6. Methods
  7. Problem Formulation
  8. Overview of the process
  9. Data pre-processing
  10. Anomaly Detection Model
  11. Network Architecture
  12. Anomaly Detection
  13. Evaluation
  14. Baselines
  15. Results
  16. ...and 1 more sections

Figures (4)

  • Figure 1: The process of how the data is used for anomaly detection.
  • Figure 2: An overview of the SSSDS4 by Miguel et al. SSSD.
  • Figure 3: Reconstruction-based detection of anomalies by SSSDS4. The red marked stars are the samples where the anomaly correctly identified anomalies.
  • Figure 4: Forecasting-based detection of anomalies by SSSDS4. The red-marked stars are the samples where the anomaly correctly identified anomalies.