3G and 4G paging success rate based mobile network anomaly detection using supervised and unsupervised learning

3G and 4G paging success rate based mobile network anomaly detection using supervised and unsupervised learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.

Bibliographic Details
Main Author: Ahasan, Md Rakibul
Other Authors: Alam, Md. Golam Robiul
Format: Thesis
Language:English
Published: Brac University 2022
Subjects:
Anomaly detection
Supervised learning
KPI
Mobile Networks
SMOTE
Unsupervised learning
K-Means
DBSCAN
HDBSCAN
Autoencoder
Algorithms
Computer network architectures
Online Access:http://hdl.handle.net/10361/17164

Internet

http://hdl.handle.net/10361/17164

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