Mining Potential Spammers from Mobile Call Logs - Zhipeng Liu; Dechang Pi; Yunfang Chen (International Journal of Distributed Sensor Networks)
With the rapid development of mobile telecommunication, voice call spam has become a growing problem in China. Many mobile phone users have become the victim of spam calls and suffered heavy financial loss. Discovering of call spammers can benefit mobile network operators as well as users. Nowadays, the popular method for the task of mining call spammers has been performed by different applications on smartphones. These applications combine manual and automatic methods to detect spammers. Although the results of these client-based solutions are quite satisfying, it is extremely unfortunate that many people still use feature phones, which can not be equipped with third party applications. In this paper, we propose a server-based solution and take a call log file as an example, to analyze the characteristics of mobile call patterns. A time-based graph model and a simple and effective call log rank (CLRank) algorithm with ranking and classification were proposed to find potential call spammers. Compared with existing methods, our model just uses link information, and thus protects user privacy to the maximum extent. Experimental results show that our proposed model can find spammers from call logs automatically, dynamically, and effectively (with 84.5~91.8% of accuracy) without any manual interventions.