Abstract
Spam calls are everywhere. According to a research study, Taiwanese wastes almost 150,000 hours on spam calls per month. Spam calls include Fraud, Harassed and Marketing. Moreover, we lost 3.7 billion NTD every year because of the Fraud call. Although there are many studies talking about spam calls detection, few of them try to classify the category of spam calls. In this research, we obtain a huge dataset about spam calls’ call logs that include the category, duration and date. First, we run data preprocess and data aggregation, then use oversampling to overcome the problem of imbalanced data. In addition, we implement multiple models of logistic regression to solve the multi-class classification, and then build models that can classify spam calls into three categories. We also use social network analysis to find out the social relationship of calls within some subgroups. In conclusion, different spam calls have exactly different behaviors. It is possible to identify them by using classification and social network analysis. However, spammers’ behavior may change as the time goes by, doing analysis once and for all is impossible. It is necessary to train new model routinely to overcome the changing behavior.