Abstract
Pseudo base stations do great harm to people’s daily lives through cheating on mobile terminals to reside in the pseudo base station and forcing users to receive spam messages. However, conducting track detection of pseudo base stations is a challenging task because there exists not only a requirement for an aggregation of millions of spam messages, but also a requirement for effective methods to express behavior patterns and trajectory characteristics of pseudo base stations. In this paper, we put forward a visualization approach for analyzing pseudo base stations. Our approach leverages valid clustering algorithm to extract distinct pseudo base stations from millions of spam messages and provides an integrated visualization system to analysis behavior patterns and trajectory characteristics. By means of case studies of real-world data, the practicability and effectiveness of the method are demonstrated.
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