Clustering of Telecommunications User Profiles for Fraud Detection and Security Enhancement in Large Corporate Networks: A case Study

Authors

Constantinos S. Hilas, Dept. of Informatics Engineering, Technological Educational Institute of Central Macedonia, GR-62124, Serres, GreeceFollow
Paris A. Mastorocostas, Dept. of Informatics Engineering, Technological Educational Institute of Central Macedonia, GR-62124, Serres, GreeceFollow
Ioannis T. Rekanos, Dept of Electrical and Electronics Engineering, School of Engineering, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, GreeceFollow

Author Country (or Countries)

Greece

Abstract

A user’s transactions with modern networks and services produce a vast amount of user related data. The byproduct of every phone call a person makes or every web page one visits is translated into a log record with usage data. By studying these log records, the user’s behavior is revealed and one may come up with clues about user preferences, identify security issues, or discover fraudulent use of the network or the service one provides. Thus, the modeling of network users’ behavior may serve as an invaluable tool for the IT manager. In this paper, many of these issues are discussed and emphasis is given on the construction of appropriate user behavior representation in telecommunications. As an example, the application of two clustering techniques is presented, with the task to identify appropriate user behavior representations (profiles) inside a large organization’s telecommunications network, in order to spot fraudulent usage. Through this study a researcher and/or the organization’s network manager may gain more insight into the problems of user profiling and fraud detection.

Recommended Citation

S. Hilas, Constantinos; A. Mastorocostas, Paris; and T. Rekanos, Ioannis (2015) "Clustering of Telecommunications User Profiles for Fraud Detection and Security Enhancement in Large Corporate Networks: A case Study," Applied Mathematics & Information Sciences: Vol. 09: Iss. 4, Article 7.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss4/7