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Smart log data analytics = technique...

Skopik, Florian.

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Smart log data analytics = techniques for advanced security analysis /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Smart log data analytics/ by Florian Skopik, Markus Wurzenberger, Max Landauer.
Reminder of title:	techniques for advanced security analysis /
Author:	Skopik, Florian.
other author:	Wurzenberger, Markus.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xv, 208 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data logging. -
Online resource:	https://doi.org/10.1007/978-3-030-74450-2
ISBN:	9783030744502

Smart log data analytics = techniques for advanced security analysis /
Skopik, Florian.

Smart log data analyticstechniques for advanced security analysis /[electronic resource] :by Florian Skopik, Markus Wurzenberger, Max Landauer. - Cham :Springer International Publishing :2021. - xv, 208 p. :ill., digital ;24 cm.

This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel machine learning techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for "online use", not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicities through log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot new events, new parameters, new correlations, new values and unknown value combinations and can run as stand-alone solution or as sensor with connection to a SIEM solution. More advanced detectors help to determine the characteristics of variable parts of log lines, specifically the properties of numerical and categorical fields. Detailed examples throughout this book allow the reader to better understand and apply the introduced techniques with open source software. Step-by-step instructions help to get familiar with the concepts and to better comprehend their inner mechanisms. A log test data set is available as free download and enables the reader to get the system up and running in no time. This book is designed for researchers working in the field of cyber security, and specifically system monitoring, anomaly detection and intrusion detection. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems. Forward-thinking practitioners, who would benefit from becoming familiar with the advanced anomaly detection methods, will also be interested in this book.

ISBN: 9783030744502

Standard No.: 10.1007/978-3-030-74450-2doiSubjects--Topical Terms:

1639275
Data logging.

LC Class. No.: QA76.9.D3385

Dewey Class. No.: 005.82

Smart log data analytics = techniques for advanced security analysis /
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245 1 0 $a Smart log data analytics $h [electronic resource] : $b techniques for advanced security analysis / $c by Florian Skopik, Markus Wurzenberger, Max Landauer.
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520 $a This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel machine learning techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for "online use", not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicities through log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot new events, new parameters, new correlations, new values and unknown value combinations and can run as stand-alone solution or as sensor with connection to a SIEM solution. More advanced detectors help to determine the characteristics of variable parts of log lines, specifically the properties of numerical and categorical fields. Detailed examples throughout this book allow the reader to better understand and apply the introduced techniques with open source software. Step-by-step instructions help to get familiar with the concepts and to better comprehend their inner mechanisms. A log test data set is available as free download and enables the reader to get the system up and running in no time. This book is designed for researchers working in the field of cyber security, and specifically system monitoring, anomaly detection and intrusion detection. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems. Forward-thinking practitioners, who would benefit from becoming familiar with the advanced anomaly detection methods, will also be interested in this book.
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650 0 $a Computer security. $3 540555
650 1 4 $a Systems and Data Security. $3 898223
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Principles and Models of Security. $3 3382356
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650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Wurzenberger, Markus. $3 3514054
700 1 $a Landauer, Max. $3 3514055
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773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-74450-2
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