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
https://cmgimpact.com/detection-of-performance-anomaly-using-desom/
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