Cluster Validation with Generalized Dunn's Indices
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Linked
Detecting spam in VoIP networks
SRUTI'05 Proceedings of the Steps to Reducing Unwanted Traffic on the Internet on Steps to Reducing Unwanted Traffic on the Internet Workshop
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering NGN user behavior for anomaly detection
Information Security Tech. Report
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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.