Anomaly Detection in LTE Mobile Network / (Record no. 9784)

MARC details
000 -LEADER
fixed length control field 06553nam a22002537a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201210b2022 a|||f bm|| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
041 0# - Language Code
Language code of text eng
Language code of abstract eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Mahmoud Adel Nour
245 1# - TITLE STATEMENT
Title Anomaly Detection in LTE Mobile Network /
Statement of responsibility, etc. Mahmoud Adel Nour
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2022
300 ## - PHYSICAL DESCRIPTION
Extent 97 p.
Other physical details ill.
Dimensions 21 cm.
500 ## - GENERAL NOTE
Materials specified Supervisor: <br/>Nashwa AbdelBaki
502 ## - Dissertation Note
Dissertation type Thesis (M.A.)—Nile University, Egypt, 2022 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:<br/>CERTIFICATION OF APPROVAL ...................................................................... iii<br/>DECLARATION ................................................................................................... vii<br/>ACKNOWLEDGEMENTS .................................................................................... ix<br/>TABLE OF CONTENTS ........................................................................................ xi<br/>LIST OF FIGURES .............................................................................................. xiii<br/>LIST OF TABLES ................................................................................................. xv<br/>ACRONYMS ....................................................................................................... xvii<br/>PUBLICATIONS .................................................................................................. xix<br/>ABSTRACT .......................................................................................................... xxi<br/>1. CHAPTER ONE HISTORICAL BACKGROUND ......................................... 1<br/>1.1. Motivation ................................................................................................. 1<br/>1.2. Background and Aims ............................................................................... 2<br/>1.3. Thesis Structure and Contribution............................................................. 4<br/>2. CHAPTER TWO STATE OF THE ART ......................................................... 7<br/>2.1. Main Streams............................................................................................. 8<br/>2.1.1. Statistical Modelling .................................................................................... 8<br/>2.1.2. Regression Scoring....................................................................................... 9<br/>2.1.3. Supervised Learning .................................................................................. 10<br/>2.1.4. Unsupervised Learning .............................................................................. 10<br/>2.1.5. Autoencoders ............................................................................................ 11<br/>2.1.6. Policy-based Reinforcement Learning ....................................................... 12<br/>xii<br/>2.2. Summary and Conclusion ....................................................................... 12<br/>3. CHAPTER THREE EXPLORATION AND EXPLOITATION OF THE LTE MOBILE NETWORK DATA ............................................................................... 15<br/>3.1. Problem Definition .................................................................................. 16<br/>3.2. Data Description ...................................................................................... 16<br/>3.3. Dataset Exploration ................................................................................. 19<br/>3.4. Summary and Conclusion ....................................................................... 28<br/>4. CHAPTER FOUR EXPERIMENTAL PROCESS AND RESULTS ............ 31<br/>4.1. Experiment Architecture ......................................................................... 32<br/>4.2. Results ..................................................................................................... 61<br/>4.3. Discussion and limitations ...................................................................... 64<br/>5. CHAPTER FIVE CONCLUSION AND FUTURE WORK .......................... 67<br/>5.1. Future Work ............................................................................................ 68<br/>References .............................................................................................................. 71
520 3# - Abstract
Abstract Abstract:<br/>The usage of anomaly detection is widely known for its benefits. It is defined as the outlier detection mechanism. The techniques added to anomaly detection is huge since the 2000s. The start of the anomaly detection was to classify the cases whether it is normal cases or anomaly cases. Over time, the time series challenges need to engage with the anomaly techniques. The time series challenges were handled with the statistical approaches at the beginning. Then the machine learning techniques added more information to the assessment. Supervised learning is used to classify the anomaly cases versus the normal cases using the available data. Then the unsupervised learning is injected to profile the anomaly cases. The unsupervised learning showed enhancement in the cases where the anomaly cases were clear enough to classify.<br/>The combination of the multivariate appeared to enhance the results with the increase in the business representation. The increase in the business representation gives the flexibility of defining multiple domains. These domains could represent the business view. We use the power of unsupervised learning enhanced by statistical modeling to solve the anomaly detection challenge.<br/>In this thesis, we propose a framework for detecting abnormal EUtranCell in LTE networks using raw counters of LTE mobile networks. The LTE mobile network is known for its huge number of cells in the network. The number of profiles in the LTE mobile network depends on how old the mobile operator is. Our method is based on using unsupervised learning to model health network KPIs in terms of revenue, performance, and customer satisfaction. This modeling gives the edge of getting the fit EUtranCell profile. We also use statistical modeling to set the severity of cases to guide mobile network operators to healthy networks. This provides flexibility in terms of operating costs relative to customer perception requirements. Our framework gives the flexibility of using any approach to assess anomaly cases.
546 ## - Language Note
Language Note Text in English, abstracts in English.
650 #4 - Subject
Subject Informatics-IFM
655 #7 - Index Term-Genre/Form
Source of term NULIB
focus term Dissertation, Academic
690 ## - Subject
School Informatics-IFM
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
650 #4 - Subject
-- 266
655 #7 - Index Term-Genre/Form
-- 187
690 ## - Subject
-- 266
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Main library Main library 09/14/2022   610/ M.N.A / 2022 09/14/2022 09/14/2022 Thesis