
Traffic Anomaly Detection
ISTE Press - Elsevier
Published on 30. October 2015
Book
Hardback
70 pages
978-1-78548-012-6 (ISBN)
Description
Traffic Anomaly Detection presents an overview of traffic anomaly detection analysis, allowing you to monitor security aspects of multimedia services. The author's approach is based on the analysis of time aggregation adjacent periods of the traffic.
As traffic varies throughout the day, it is essential to consider the concrete traffic period in which the anomaly occurs. This book presents the algorithms proposed specifically for this analysis and an empirical comparative analysis of those methods and settle a new information theory based technique, named "typical day analysis".
As traffic varies throughout the day, it is essential to consider the concrete traffic period in which the anomaly occurs. This book presents the algorithms proposed specifically for this analysis and an empirical comparative analysis of those methods and settle a new information theory based technique, named "typical day analysis".
Reviews / Votes
"...their focus is on the theoretical aspects of determining anomalous traffic...it's bound to be of interest to those developing security solutions." --Network SecurityMore details
Language
English
Place of publication
United Kingdom
Target group
Professional and scholarly
Scientific and Engineering communities working on Anomaly detection in the context of Network Security. In particular, early researchers, post-docs and engineers with an interest in this field.
Product notice
Laminated cover
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 8 mm
Weight
278 gr
ISBN-13
978-1-78548-012-6 (9781785480126)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Antonio Cuadra-Sánchez | Javier Aracil
Traffic Anomaly Detection
E-Book
10/2015
Elsevier
€57.95
Available for download
Persons
Antonio Cuadra-Sanchez is a Telecommunications Engineer (MSc) from the University of Cantabria (Spain). He also holds a Masters degree in Computing and communications from the University Autonoma of Madrid (Spain). He works as a research project manager and technology advisor for QoS and QoE in Indra. He has taught different courses of signalling protocols and networks (SS7, GSM, GPRS, UMTS, IMS and IPTV) in Telefonica R&D, Telefonica Spain and the Americas. He has published over 70 articles as much for the European organisms of regulation as for Telefonica Group, produced workshops and scientific and regulation book chapters, and has participated in lectures for different national and international conferences, including TeleManagement Forum, ETSI and IEEE.He currently leads the Celtic NOTTS projectand co-leads the Customer Experience Management (CEM) Implementation Guide at the TeleManagement Forum. Javier Aracil received the M.Sc. and Ph.D. degrees (Honors) from Technical University of Madrid in 1993 and 1995, both in Telecommunications Engineering. In 1995 he was awarded with a Fulbright scholarship and was appointed as a Postdoctoral Researcher of the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. In 1998 he was a research scholar at the Center for Advanced Telecommunications, Systems and Services of The University of Texas at Dallas. He has been an associate professor for University of Cantabria and Public University of Navarra and he is currently a full professor at Universidad Autonoma de Madrid, Madrid, Spain. His research interest are in optical networks and performance evaluation of communication networks. He has authored more than 100 papers in international conferences and journals.
Author
Indra Sistemas, S.A. / Universidad Autonoma de Madrid, Spain
Professor, Universidad Autonoma de Madrid, Madrid, Spain
Content
1. Theoretical anomaly detection methods. Set of algorithms proposed for this analysis: the most used SCC (CUSUM), the two main tests of goodness-of-fit and Mutual Information.2. Finding the optimal aggregation period for a time series of Internet traffic3. Comparative analysis of traffic anomaly detection methods4. Proposal of a new information-theory based technique (typical day analysis)5. Conclusions