Interpretable unsupervised anomaly detection for RAN cell trace analysis

Ashima Chawla, Paul Jacob, Saman Feghhi, Devashish Rughwani, Sven Van Der Meer, Sheila Fallon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

The high complexity of modern communication networks requires an increasing degree of automation for performance and fault management tasks. A key task is the classification and identification of anomalous operation modes (and faults). This is important to separate them from normal operation conditions. In addition, these diagnoses should be interpretable by domain experts to (a) gain acceptance by these experts and (b) support effective root cause analysis and localisation. In this paper, we investigate the analysis of multivariant network data in order to identify anomalous data instances. Root cause analysis benefits from this by filtering features whose values lead (to some extent) to such anomalies. We are using Deep Neural Networks (DNNs), a powerful tool for anomaly detection in the telecommunication domain. We demonstrate the effectiveness of autoencoders (an unsupervised technique) to detect multivariant anomalies and anomalous features. To overcome the black box nature of neural networks (and thus increase their acceptance by domain experts), we apply SHapley Additive exPlanations (SHAP), which are used to explain the output model of a neural network.

Original languageEnglish
Title of host publication16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020
EditorsNur Zincir-Heywood, Mehmet Ulema, Muge Sayit, Stuart Clayman, Myung-Sup Kim, Cihat Cetinkaya
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783903176317
DOIs
Publication statusPublished - 2 Nov 2020
Event16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020 - Virtual, Izmir, Turkey
Duration: 2 Nov 20206 Nov 2020

Publication series

Name16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020

Conference

Conference16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020
Country/TerritoryTurkey
CityVirtual, Izmir
Period2/11/206/11/20

Keywords

  • Autoencoders
  • Correlation
  • DNNs
  • SHAP

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