eBook
Smart Log Data Analytics
| Τίτλος: | Smart Log Data Analytics |
|---|---|
| Περιγραφή: | 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 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 periodicitiesthrough 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 determines 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. |
| Συγγραφείς: | Florian Skopik, Markus Wurzenberger, Max Landauer |
| Resource Type: | eBook. |
| Θέματα: | Data logging, Computer security |
| Categories: | COMPUTERS / Security / General, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Data Science / Data Analytics, MATHEMATICS / Probability & Statistics / General |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: edsebk DbLabel: eBook Index An: 3022692 RelevancyScore: 956 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 955.975708007813 |
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| Items | – Name: Title Label: Title Group: Ti Data: Smart Log Data Analytics – Name: Abstract Label: Description Group: Ab Data: 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 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 periodicitiesthrough 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 determines 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Florian+Skopik%22">Florian Skopik</searchLink><br /><searchLink fieldCode="AR" term="%22Markus+Wurzenberger%22">Markus Wurzenberger</searchLink><br /><searchLink fieldCode="AR" term="%22Max+Landauer%22">Max Landauer</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+logging%22">Data logging</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+security%22">Computer security</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Security+%2F+General%22">COMPUTERS / Security / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+General%22">COMPUTERS / Artificial Intelligence / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+Computer+Vision+%26+Pattern+Recognition%22">COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Data+Analytics%22">COMPUTERS / Data Science / Data Analytics</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3022692 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 005.82 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Data logging Type: general – SubjectFull: Computer security Type: general Titles: – TitleFull: Smart Log Data Analytics Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Florian Skopik – PersonEntity: Name: NameFull: Markus Wurzenberger – PersonEntity: Name: NameFull: Max Landauer – PersonEntity: Name: NameFull: Florian Skopik – PersonEntity: Name: NameFull: Markus Wurzenberger – PersonEntity: Name: NameFull: Max Landauer IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2021 – D: 30 M: 08 Type: profile Y: 2021 Identifiers: – Type: isbn-print Value: 9783030744496 – Type: isbn-electronic Value: 9783030744502 Titles: – TitleFull: Smart Log Data Analytics Type: main |
| ResultId | 1 |