Proactive detection of voice phishing networks using call log analysis and machine learning

Voice phishing represents a rapidly evolving form of cyber-enabled financial fraud that exploits telecommunications networks to deceive victims. Traditional reactive policing approaches face significant limitations in preventing such crimes in real time. This study introduces a data-driven framework for the proactive detection of phone numbers linked to voice phishing operations using large-scale call log data from South Korea. Behavioral features derived from call metadata were utilized to capture distinctive communication patterns that differentiate fraudulent users from legitimate ones. A stepwise logistic regression model was initially employed to identify key predictors of fraudulent behavior, followed by advanced machine learning models-including random forest, gradient boosting, and Multi-Layer Perceptron (MLP)-for classification. The results reveal that voice phishing numbers display unique behavioral characteristics, such as concentrated weekday activity, shorter call durations, higher outgoing call ratios, and a preference for Mobile Virtual Network Operator (MVNO) usage. The proposed models achieved high performance, exceeding 95% accuracy and 97% recall, demonstrating their robustness in detecting suspicious numbers. These findings underscore the potential of integrating artificial intelligence and behavioral analytics into proactive fraud detection systems and contribute to the development of early-warning mechanisms for preventing telecommunication-based financial crimes.