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In den WarenkorbZustand: New. pp. 150.
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Sprache: Englisch
Verlag: World Scientific Publishing Company, Incorporated, 2020
ISBN 10: 9811220107 ISBN 13: 9789811220104
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In den WarenkorbZustand: New. pp. 280.
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In den WarenkorbZustand: New. In English.
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Sprache: Englisch
Verlag: World Scientific Publishing Company, 2016
ISBN 10: 9814678414 ISBN 13: 9789814678414
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Sprache: Englisch
Verlag: Wiley-Scrivener (edition 1), 2022
ISBN 10: 1119791790 ISBN 13: 9781119791799
Anbieter: BooksRun, Philadelphia, PA, USA
Hardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2021
ISBN 10: 1839530596 ISBN 13: 9781839530593
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Verlag: The Institution of Engineering and Technology, 2021
ISBN 10: 1839530642 ISBN 13: 9781839530647
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Verlag: Inst of Engineering & Technology, 2021
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In den WarenkorbHardcover. Zustand: Brand New. 359 pages. 9.25x6.00x1.00 inches. In Stock.
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Verlag: Inst of Engineering & Technology, 2021
ISBN 10: 1839530596 ISBN 13: 9781839530593
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In den WarenkorbHardcover. Zustand: Brand New. 384 pages. 9.50x6.25x1.00 inches. In Stock.
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In den WarenkorbHardcover. Zustand: Brand New. 400 pages. 9.09x6.34x1.14 inches. In Stock.
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In den WarenkorbZustand: New. pp. 525.
Zustand: Gut. Zustand: Gut | Seiten: 296 | Sprache: Englisch | Produktart: Bücher | This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud¿s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS¿s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS¿s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
Anbieter: moluna, Greven, Deutschland
Zustand: New. A. Jaya, PhD, Professor in the Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, India. She has published more than 90 research articles in international journals.K. Kalaiselvi, PhD, is a Professor and Hea.
Sprache: Englisch
Verlag: INSTITUTION OF ENGINEERING & T, 2021
ISBN 10: 1839530642 ISBN 13: 9781839530647
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In den WarenkorbGebunden. Zustand: New. KlappentextThis comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that.
Sprache: Englisch
Verlag: INSTITUTION OF ENGINEERING & T, 2021
ISBN 10: 1839530596 ISBN 13: 9781839530593
Anbieter: moluna, Greven, Deutschland
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In den WarenkorbZustand: New. KlappentextThis comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that.
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EUR 233,22
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In den WarenkorbZustand: New.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Handbook of Big Data and Analytics in Accounting and Auditing | Tarek Rana (u. a.) | Taschenbuch | xxii | Englisch | 2024 | Springer | EAN 9789811944628 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Business Science Reference, 2014
ISBN 10: 1466672722 ISBN 13: 9781466672727
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 242,39
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In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Springer International Publishing, 2022
ISBN 10: 3030747557 ISBN 13: 9783030747558
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). Theauthorspropose a deep learning-based approach to process cloud's log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposesa robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in thesixthchapter. Theauthorsevaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS's cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS's cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated.This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. Theauthorsdemonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well asa proposedclustering approach to detect IoT malware in the last two chapters.This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
Sprache: Englisch
Verlag: Springer International Publishing, 2021
ISBN 10: 3030747522 ISBN 13: 9783030747527
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). Theauthorspropose a deep learning-based approach to process cloud's log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposesa robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in thesixthchapter. Theauthorsevaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS's cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS's cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated.This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. Theauthorsdemonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well asa proposedclustering approach to detect IoT malware in the last two chapters.This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.