• home
• current students
• graduate
• events

Presenter: Amirreza Balouchi
Supervisor:

Date: Tue, December 9, 2025
Time: 12:00:00 - 00:00:00
Place: Zoom - see below.

ABSTRACT

Join Zoom Meeting
https://uvic.zoom.us/j/87392272186?pwd=2ssHYuttIMQ69kX7UKCkSTFRYPkbKB.1

Meeting ID: 873 9227 2186
Password: 294335
One tap mobile
+16475580588,,87392272186# Canada
+17789072071,,87392272186# Canada

Dial by your location
        +1 647 558 0588 Canada
        +1 778 907 2071 Canada
Meeting ID: 873 9227 2186
Find your local number: https://uvic.zoom.us/u/kb5m1YrfpF

Note: Please log in to Zoom via SSO and your UVic Netlink ID

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.