Identifying abnormal pattern in cellular communication flows

Goergen, David; Mendiratta, Veena; State, Radu; Engel, Thomas

Reference : Identifying abnormal pattern in cellular communication flows


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/15350

Title :	Identifying abnormal pattern in cellular communication flows
Language :	English
Author, co-author :	Goergen, David [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Mendiratta, Veena []
	State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Oct-2013
Main document title :	Proceedings of IPTComm 2013
Publisher :	ACM
Peer reviewed :	Yes
Audience :	International
ISBN :	978-1-4503-2672-8
Event name :	7th International Conference on Principles, Systems and Applications of IP Telecommunications
Event date :	October 15-17, 2013
Event organizer :	Illinois Institute of Technology
Event place (city) :	Chicago
Event country :	USA
Keywords :	[en] Call data record analysis ; Big Data processing ; call pattern detection
Abstract :	[en] Analyzing communication flows on the network can help to improve the overall quality it provides to its users and allow the operators to detect abnormal patterns and react accordingly. In this paper we consider the analysis of large volumes of cellular communications records. We propose a method that detects abnormal communications events covering call data record volumes, comprising a country-level data set. We detect patterns by calculating a weighted average using a sliding window with a fixed period and correlate the results with actual events happening at that time. We are able to successfully detect several events using a data set provided by a mobile phone operator, and suggest examples of future usage of the outcome such as real time pattern detection and possible visualisation for mobile phone operators.
Permalink :	http://hdl.handle.net/10993/15350

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