By Topic

A Distribution-Based Approach to Anomaly Detection and Application to 3G Mobile Traffic

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
D'Alconzo, A. ; Forschungszentrum Telecommunikation Wien, Vienna, Austria ; Coluccia, A. ; Ricciato, F. ; Romirer-Maierhofer, P.

In this work we present a novel scheme for statistical-based anomaly detection in 3G cellular networks. The traffic data collected by a passive monitoring system are reduced to a set of per-mobile user counters, from which time-series of unidimensional feature distributions are derived. An example of feature is the number of TCP SYN packets seen in uplink for each mobile user in fixed-length time bins. We design a change-detection algorithm to identify deviations in each distribution time-series. Our algorithm is designed specifically to cope with the marked non-stationarities, daily/weekly seasonality and longterm trend that characterize the global traffic in a real network. The proposed scheme was applied to the analysis of a large dataset from an operational 3G network. Here we present the algorithm and report on our practical experience with the analysis of real data, highlighting the key lessons learned in the perspective of the possible adoption of our anomaly detection tool on a production basis.

Published in:

Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE

Date of Conference:

Nov. 30 2009-Dec. 4 2009