Full metadata record
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.contributor.advisorZhang, Jun (EIE)en_US
dc.creatorHussain, Bilal-
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleArtificial intelligence-based anomaly detection for the efficient management and security of the future cellular networksen_US
dcterms.abstractExisting communication systems are getting more intricate and a morass to manage which is galvanized by ever-increasing network capacity demand, device density, and the data traffic. Network operators in the United States alone deplete over USD 15 billion yearly to handle cellular outages, incurring escalated operational expenditure (OPEX) as a result. Furthermore, congestion in a cell degrades subscriber quality-of-experience and quality-of-service which results in an increased churn rate and subsequently reduced operator's revenue. Besides the management aspect, the security of the cellular network is paramount to prevent cyber-attacks against its infrastructure not just for its primary subscribers but also for the fact that the networks can be exploited as a proxy to attack the connected cyber-physical systems (CPSs). To unlock the full extent of 6G networks, artificial intelligence (AI)-empowerment has paramount potential for efficient network management and preventing cyber-attacks against their infrastructure. Based on this backdrop, this thesis applies AI techniques to achieve a primary objective of an efficient, scalable, and timely detection of outages and the situation leading towards congestion in a cell under the context of cellular network management. It also aims to achieve a secondary objective of detecting various cyber-attacks towards the availability of cellular network services in the context of cyber-security of cellular infrastructure and CPSs. Cell outages, situation leading towards congestion in a cell, and cyber-attacks are treated as anomalies in this work and various machine learning (subset of AI) models and data analytic tools are utilized to detect them by leveraging real subscriber data based on call detail records (CDRs) extracted from a 4G LTE-A network.en_US
dcterms.extentxxv, 173 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHAnomaly detection (Computer security)en_US
dcterms.LCSHMachine learningen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

Files in This Item:
File Description SizeFormat 
5971.pdfFor All Users10.96 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show simple item record

Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/11455