Items related to Handbook of Big Data Analytics and Forensics
- PublisherSpringer
- Publication date2021
- ISBN 10 3030747522
- ISBN 13 9783030747527
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages295
- EditorChoo Kim-Kwang Raymond, Dehghantanha Ali
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Handbook of Big Data Analytics and Forensics
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Handbook of Big Data Analytics and Forensics
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Handbook of Big Data Analytics and Forensics
Published by
Springer International Publishing, 2021
ISBN 10: 3030747522
ISBN 13: 9783030747527
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Seller: moluna, Greven, Germany
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Condition: 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 research opportunitiesPresents cyber. Seller Inventory # 458554171
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Handbook of Big Data Analytics and Forensics
Published by
Springer International Publishing Dez 2021, 2021
ISBN 10: 3030747522
ISBN 13: 9783030747527
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Buch. Condition: 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 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. 296 pp. Englisch. Seller Inventory # 9783030747527
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Handbook of Big Data Analytics and Forensics
Published by
Springer International Publishing, 2021
ISBN 10: 3030747522
ISBN 13: 9783030747527
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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. Seller Inventory # 9783030747527
Quantity: 1 available
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Handbook of Big Data Analytics and Forensics
Published by
Springer-Nature New York Inc, 2022
ISBN 10: 3030747522
ISBN 13: 9783030747527
New
Hardcover
Seller: Revaluation Books, Exeter, United Kingdom
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Hardcover. Condition: Brand New. 295 pages. 9.25x6.10x1.57 inches. In Stock. Seller Inventory # x-3030747522
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