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Mining Potential Spammers from Mobile Call Logs

机译:从移动电话记录中挖掘潜在的垃圾邮件发送者

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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.
机译:随着移动通信的飞速发展,语音垃圾邮件已经成为中国日益严重的问题。许多手机用户已成为垃圾邮件的受害者,并遭受了严重的经济损失。发现垃圾邮件发送者可以使移动网络运营商和用户受益。如今,用于挖掘垃圾邮件发送者任务的流行方法已由智能手机上的不同应用程序执行。这些应用程序结合了手动和自动方法来检测垃圾邮件发送者。尽管这些基于客户端的解决方案的结果令人非常满意,但非常不幸的是,许多人仍在使用功能电话,而这些电话无法配备第三方应用程序。在本文中,我们提出了一种基于服务器的解决方案,并以呼叫日志文件为例,分析了移动呼叫模式的特征。为了找到潜在的垃圾邮件发送者,提出了基于时间的图模型以及具有排名和分类功能的简单有效的呼叫日志等级(CLRank)算法。与现有方法相比,我们的模型仅使用链接信息,从而最大程度地保护用户隐私。实验结果表明,我们提出的模型可以自动,动态,有效地(准确度为84.5%至91.8%)从呼叫日志中找到垃圾邮件发送者。

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