9783030747527 - handbook of big data analytics and forensics (16 resultados)
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 192,59
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Buch. Condición: 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.
Handbook of Big Data Analytics and Forensics
Choo, Kim-Kwang Raymond (Editor)/ Dehghantanha, Ali (Editor)
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 295 pages. 9.25x6.10x1.57 inches. In Stock.
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EUR 293,29
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 150,28
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Condición: new. Questo è un articolo print on demand.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 162,51
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers advances in big data analytics and digital forensics from an interdisciplinary lensProvides a comprehensive review and bibliometric analysis of big data and IoT applications, as well as future researc…h opportunitiesPresents cyber.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 192,59
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 s…ecurity, 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. 296 pp. Englisch.
Idioma: Inglés
Editorial: Springer International Publishing, Springer Nature Switzerland Dez 2021 2021
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 192,59
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 secur…ity, 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 276,60
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 273,01
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Condición: New. PRINT ON DEMAND.
