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Handbook of Big Data Analytics and Forensics (Hardcover, 1st ed. 2022)
Loot Price: R4,909
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Handbook of Big Data Analytics and Forensics (Hardcover, 1st ed. 2022)
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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.
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