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Traffic anomaly detection

Cua-Sánchez, Antonio,

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Traffic anomaly detection

Record Type:	Electronic resources : Monograph/item
Title/Author:	Traffic anomaly detection/ Antonio Cua-Sánchez, Javier Aracil.
Author:	Cua-Sánchez, Antonio,
other author:	Aracil, Javier,
Published:	London, UK :ISTE, Ltd. ; : 2015.,
Description:	1 online resource :ill.
[NT 15003449]:	Front Cover -- Traffic Anomaly Detection -- Copyright -- Contents -- Introduction
[NT 15003449]:	Chapter 1: Introduction to Traffic Anomaly Detection Methods 1.1. Cumulative Sum Control Charts (CUSUM) -- 1.2. Tests of Goodness-of-fit -- 1.3. Mutual Information (MI)
[NT 15003449]:	Chapter 2: Finding the Optimal Aggregation Period 2.1. Introduction -- 2.2. State of the Art
[NT 15003449]:	2.3. Macroscopic Observation of Traffic 2.4. Average-Day Analysis -- 2.5. Conclusion -- Chapter 3: Comparative Analysis of Traffic Anomaly Detection Methods
[NT 15003449]:	3.1. Introduction 3.2. State of the Art -- 3.3. Average-Day Preliminary Analysis -- 3.4. Proposed Change Point Detection Algorithms
Subject:	Signal detection - Statistical methods. -
Online resource:	https://www.sciencedirect.com/science/book/9781785480126
ISBN:	9780081008072 (electronic bk.)

Traffic anomaly detection
Cua-Sánchez, Antonio,

Traffic anomaly detection[electronic resource] /Antonio Cua-Sánchez, Javier Aracil. - London, UK :ISTE, Ltd. ;2015. - 1 online resource :ill.

Includes bibliographical references and index.

Front Cover -- Traffic Anomaly Detection -- Copyright -- Contents -- Introduction

This book 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."

ISBN: 9780081008072 (electronic bk.)Subjects--Topical Terms:

3462812
Signal detection
--Statistical methods.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: TK5102.5

Dewey Class. No.: 621.3822

Traffic anomaly detection
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020 $a 0081008074 (electronic bk.)
020 $a 9781785480126
035 $a (OCoLC)927438026
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040 $a N$T $b eng $c N$T $d N$T $d IDEBK $d OCLCO $d BTCTA $d YDXCP $d CDX $d EBLCP $d OCLCO $d OCLCF $d OCLCO $d OPELS $d OCLCO $d UIU $d MERUC $d IDB $d VGM $d OCLCQ $d U3W $d WRM $d D6H $d CEF $d EZ9 $d OCLCQ $d AU@ $d OCLCQ
041 0 $a eng
050 4 $a TK5102.5
082 0 4 $a 621.3822 $2 23
100 1 $a Cua-Sánchez, Antonio, $e author. $3 3462810
245 1 0 $a Traffic anomaly detection $h [electronic resource] / $c Antonio Cua-Sánchez, Javier Aracil.
260 $a London, UK : $b ISTE, Ltd. ; $a Kidlington, Oxford, UK : $b Elsevier, $c 2015.
300 $a 1 online resource : $b ill.
504 $a Includes bibliographical references and index.
505 0 $a Front Cover -- Traffic Anomaly Detection -- Copyright -- Contents -- Introduction
505 8 $a Chapter 1: Introduction to Traffic Anomaly Detection Methods 1.1. Cumulative Sum Control Charts (CUSUM) -- 1.2. Tests of Goodness-of-fit -- 1.3. Mutual Information (MI)
505 8 $a Chapter 2: Finding the Optimal Aggregation Period 2.1. Introduction -- 2.2. State of the Art
505 8 $a 2.3. Macroscopic Observation of Traffic 2.4. Average-Day Analysis -- 2.5. Conclusion -- Chapter 3: Comparative Analysis of Traffic Anomaly Detection Methods
505 8 $a 3.1. Introduction 3.2. State of the Art -- 3.3. Average-Day Preliminary Analysis -- 3.4. Proposed Change Point Detection Algorithms
520 $a This book 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."
588 0 $a Online resource; title from PDF title page (EBSCO, viewed November 5, 2015).
650 0 $a Signal detection $x Statistical methods. $3 3462812
650 0 $a Signal detection $x Mathematical models. $3 3462813
650 0 $a Signal processing $x Statistical methods. $3 649787
650 0 $a Signal processing $x Mathematical models. $3 678506
650 0 $a Computer networks. $3 539554
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Aracil, Javier, $e author. $3 3462811
856 4 0 $u https://www.sciencedirect.com/science/book/9781785480126