樱花影视

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Faculty of Graduate Studies

Amirreza Balouchi

  • BSc (Isfahan University of Technology, 2018)

Notice of the Final Oral Examination for the Degree of Master of Applied Science

Topic

Wangiri Fraud Detection: A Comprehensive Approach to Unlabeled Telecom Data

Department of Electrical and Computer Engineering

Date & location

  • Friday, December 12, 2025

  • 10:00 A.M.

  • Virtual Defence

Reviewers

Supervisory Committee

  • Dr. Amirali Baniasadi, Department of Electrical and Computer Engineering, UVic (Supervisor)

  • Dr. Mihai Sima, Department of Electrical and Computer Engineering, UVic (Member) 

External Examiner

  • Dr. Jens Weber, Department of Computer Science, 樱花影视 

Chair of Oral Examination

  • Dr. Nikki Macdonald, School of Public Administration, UVic

     

Abstract

Wangiri fraud is a pervasive telecommunications scam that exploits missed calls to lure victims into returning calls to premium-rate numbers, causing substantial financial losses for network operators and consumers. This study presents a machine learning framework for detecting Wangiri fraud in highly imbalanced and unlabeled Call Detail Record (CDR) datasets. The framework employs an unsupervised labeling method based on domain-driven heuristics and advanced feature engineering to capture temporal, geographic, and behavioral patterns indicative of fraudulent activity. To address class imbalance, resampling techniques, including the Synthetic Minority Oversampling Technique (SMOTE), Random Undersampling (RUS), and their hybrid variant, are systematically evaluated. Five classifier families (Logistic Regression, Decision Trees, Random Forests, XGBoost, and Multi-Layer Perceptrons) are benchmarked with and without isotonic and sigmoid probability calibration. Results show that ensemble methods, particularly Random Forest and XGBoost, achieve near-perfect performance, with accuracy exceeding 0.99 on balanced datasets while maintaining interpretability. The proposed pipeline offers a scalable and practical solution for Wangiri fraud detection, enabling operators to mitigate financial risks and enhance network resilience. 

Keywords: Wangiri fraud, Machine learning, Class imbalance, Feature engineering, XGBoost, SHAP, Fraud detection, F1-score

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