IMPACT2021 | Detection of Performance Anomaly in Mobile Network Node Entities in Evolved Packet Core Network Using Deep Embedded Self Organizing Map (DESOM) - Jayanta Choudhury, Anila Joshi - Computer Measurement Group

IMPACT2021 | Detection of Performance Anomaly in Mobile Network Node Entities in Evolved Packet Core Network Using Deep Embedded Self Organizing Map (DESOM) – Jayanta Choudhury, Anila Joshi

IMPACT 2021 Proceedings
January 29, 2021
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February 1, 2021

IMPACT2021 | Detection of Performance Anomaly in Mobile Network Node Entities in Evolved Packet Core Network Using Deep Embedded Self Organizing Map (DESOM) – Jayanta Choudhury, Anila Joshi

Deep Embedded Self Organizing Map (DESOM), a hybrid Deep Neural Network based Autoencoder-Decoder (AE-DE) with an embedded Self Organizing Map (SOM), is applied successfully for the first time to detect anomaly in the performance metrics of mobile network entities with over 94% accuracy. SOM has been widely used in many areas for anomaly detection such as fraud detection, intrusion detection, etc. DESOM is a recent enhancement of SOM but not evaluated as practical solution for real problems prior to this work. Several novel methods to detect concept drift using the intrinsic features of DESOM have been incorporated in the complete solution pipeline.

Speaker
Jayanta Choudhury
Senior Data Scientist
Ericsson Inc.
Santa Clara, California United States

Anila Joshi
Sr. Data Science Manager
Ericsson Inc.
Santa Clara, California United States

Track
Performance Engineering and DevOps

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