DeMalC: A Feature-rich Machine Learning Framework for Malicious Call Detection

Malicious phone call is a plague, in which unscrupulous salesmen or criminals make to acquire money illegally from the victims. As a result, there has been broad interest in deveploing systems to make the end-users vigilant when receiving such phone calls. Typically, these systems justify the phone numbers either by the crowd-generated blacklist or exploiting the features via machine learning techniques. However, the former is frail due to the rare and lazy crowd, while the later suffers from the scarcity of effective features. In this work, we propose a solution named DeMalC to address those problems by applying the machine learning algorithmm on a novel set of discriminative features. These features consist of properties and behaviors that are powerful enough to characterize phone numbers from different perspectives. We extensively evaluated our solution, i.e., DeMalC, using massive call detail records. The experimental result shows the effectiveness of our extracted features. Capable of achieving 91.86% overall accuracy and 79.34% F1-score on the detection of malicious phone numbers, the DeMalC has been deployed online and demonstrated to be a competitive solution for detecting malicious calls.