Telephony SPAM is a well-known phenomenon, with unique characteristics and patterns that differentiate it from other more wide spread forms of SPAM. However, until now, there has been little work to collect and meaningfully show statistical data about realworld Telephony Spam patterns. In this paper we propose Beesterix, a framework that allows us to obtain a real grasp on the phenomenon by recording real calls, analyzing them and finally presenting the results through a set of intuitive and meaningful visualization tools. Our framework comprises of a telephony honeypot that records details about the calls and the calls themselves routed to our server using SIP trunking, the data gathered is then analyzed and presented using two visualization tools: first, an aggregating tool that, using Google Earth, geo-locates each call and show interactively how they distribute in time; second, a real-time tool that dynamically pinpoints incoming calls on a map, giving an estimation of the likelihood for the call to be spam We believe that through such a framework, we can achieve our goal of obtaining a meaningful way to depict possible patterns used by spammers and at the same time, enabling the users to improve their ability of making educated guesses on the nature incoming calls.
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