Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

Hassanzadeh, Reza (2014) Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. PhD thesis, Queensland University of Technology.

Abstract

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Impact and interest:

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Full-text downloads:

428 since deposited on 16 Dec 2014
219 in the past twelve months

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ID Code: 78679
Item Type: QUT Thesis (PhD)
Supervisor: Nayak, Richi & Stebila, Douglas
Keywords: Anomaly Detection, Fuzzy Logig, Data Mining, Data Graph, Graph Theory
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 16 Dec 2014 03:01
Last Modified: 08 Sep 2015 06:55

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