Handbook big data analytics (63 Ergebnisse)

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Zustand: New. pp. 280.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. In English.

VIII, 538 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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.

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.

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.

Zustand: New. In English.

Zustand: New. In English.

Hardcover. Zustand: Brand New. 359 pages. 9.25x6.00x1.00 inches. In Stock.

Hardcover. Zustand: Brand New. 384 pages. 9.50x6.25x1.00 inches. In Stock.

Hardcover. Zustand: As New. New and unread.

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.…

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Handbook of Big Data Analytics and Forensics | Ali Dehghantanha (u. a.) | Taschenbuch | viii | Englisch | 2022 | Springer Nature Switzerland | EAN 9783030747558 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Gebundene Ausgabe. Zustand: Sehr gut. Gebraucht - Sehr gut SG - Ungelesenes Mängelexemplar, gestempelt, mit leichten Lagerspuren - Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.…

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. …