Traffic Anomaly Detection

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ISBN/EAN: 9780081008072
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'. - A new information-theory based technique for traffic anomaly detection (typical day analysis) - Introductory chapters to anomaly detection methods including control charts, tests of goodness-of-fit Mutual Information - Contains comparative analysis of traffic anomaly detection methods

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.
Autor: Antonio Cuadra-S nchez, Javier Aracil
EAN: 9780081008072
eBook Format: ePUB/PDF
Sprache: Englisch
Produktart: eBook
Veröffentlichungsdatum: 31.10.2015
Kategorie:
Schlagworte: Network algorithm anomaly entropy-based goodness-of-fit pattern statistical control charts typical day

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