Handbook of Big Data Analytics and Forensics

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 clouds 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.

Format: Paperback / softback
Length: 287 pages
Publication date: 04 December 2022
Publisher: Springer Nature Switzerland AG

This comprehensive handbook delves into the challenges and limitations of existing solutions in big data analytics and digital forensics. It presents cutting-edge advancements from both academia and industry, covering a wide range of topics.

The second chapter provides a comprehensive review of the literature on IoT security, privacy, and forensics, with a particular focus on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud log data and mitigate enumeration attacks in the third chapter.

In the fourth chapter, a robust fuzzy learning model is proposed to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. The fifth chapter introduces an advanced and fair clustering approach for industrial data, capable of training with vast volumes of data in near-linear time. Additionally, an adaptive deep learning model is presented to detect cyberattacks targeting cyber physical systems (CPS).

Chapter 7 evaluates the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS), while chapter 8 presents a robust fuzzy Bayesian approach for ICSs cyber threat hunting. The handbook also assesses the performance of supervised machine learning methods in identifying cyberattacks against CPS.

Furthermore, it evaluates the performance of a scalable clustering algorithm for CPSs cyber threat hunting and examines the usefulness of machine learning algorithms for MacOS malware detection.

The handbook concludes by evaluating the performance of various machine learning techniques to detect Internet of Things (IoT) malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models.

In summary, this handbook is a valuable resource for researchers, practitioners, and students interested in the field of big data analytics and digital forensics. It provides a comprehensive overview of the latest advancements and offers practical insights into addressing the complex challenges and limitations faced in these domains.

Weight: 456g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030747558
Edition number: 1st ed. 2022