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Anomaly detection and root cause diagnosis in cellular networks

Maha Mdini 1, 2
2 ADOPNET - Advanced technologies for operated networks
UR1 - Université de Rennes 1, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES, IMT Atlantique - IMT Atlantique Bretagne-Pays de la Loire
Abstract : With the evolution of automation and artificial intelligence tools, mobile networks havebecome more and more machine reliant. Today, a large part of their management tasks runs inan autonomous way, without human intervention. In this thesis, we have focused on takingadvantage of the data analysis tools to automate the troubleshooting task and carry it to a deeperlevel. To do so, we have defined two main objectives: anomaly detection and root causediagnosis. The first objective is about detecting issues in the network automatically withoutincluding expert knowledge. To meet this objective, we have proposed an algorithm, WatchmenAnomaly Detection (WAD), based on pattern recognition. It learns patterns from periodic timeseries and detect distortions in the flow of new data. The second objective aims at identifying theroot cause of issues without any prior knowledge about the network topology and services. Toaddress this question, we have designed an algorithm, Automatic Root Cause Diagnosis (ARCD)that identifies the roots of network issues. ARCD is composed of two independent threads: MajorContributor identification and Incompatibility detection. WAD and ARCD have been proven to beeffective. However, many improvements of these algorithms are possible.
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Submitted on : Thursday, October 3, 2019 - 1:24:35 PM
Last modification on : Wednesday, August 5, 2020 - 3:42:58 AM


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  • HAL Id : tel-02304602, version 1


Maha Mdini. Anomaly detection and root cause diagnosis in cellular networks. Artificial Intelligence [cs.AI]. Ecole nationale supérieure Mines-Télécom Atlantique, 2019. English. ⟨NNT : 2019IMTA0144⟩. ⟨tel-02304602⟩



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