An anomaly-based approach to the analysis of the social behavior of VoIP users

In this paper we present the results of a study we recently conducted by analyzing a large data set of VoIP Call Detail Records (CDRs), provided by an Italian telecom operator. The objectives of this study were twofold: (i) first, to provide a representation of users behavior, as well as of their mutual interaction and communication patterns, allowing to identify certain easily separable user categories; and (ii) second, to design and implement a framework calculating such a representation starting from CDR, capable of operating within certain time constraints, and grouping users using unsupervised techniques. The paper shows how we can reliably identify behavioral patterns associated with the most common anomalous behaviors of VoIP users. It also exploits the expressive power of relational graphs in order to both validate the results of the unsupervised analysis and ease their interpretation by human operators.