Best Research Books in Big Data Analytics and Forensics

Author(s) Name: Kim-Kwang Raymond Choo, Ali Dehghantanha

About the Book:

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.
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.
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.

Table of Contents

Big Data Analytics and Forensics: An Overview

IoT Privacy, Security and Forensics Challenges: An Unmanned Aerial Vehicle (UAV) Case Study

Detection of Enumeration Attacks in Cloud Environments Using Infrastructure Log Data

Cyber Threat Attribution with Multi-View Heuristic Analysis

Security of Industrial Cyberspace: Fair Clustering with Linear Time Approximation

Adaptive Neural Trees for Attack Detection in Cyber Physical Systems

Evaluating Performance of Scalable Fair Clustering Machine Learning Techniques in Detecting Cyber Attacks in Industrial Control Systems

Fuzzy Bayesian Learning for Cyber Threat Hunting in Industrial Control Systems

Cyber-Attack Detection in Cyber-Physical Systems Using Supervised Machine Learning

Evaluation of Scalable Fair Clustering Machine Learning Methods for Threat Hunting in Cyber-Physical Systems

Evaluation of Supervised and Unsupervised Machine Learning Classifiers for Mac OS Malware Detection

Evaluation of Machine Learning Algorithms on Internet of Things (IoT) Malware Opcodes

Mac OS X Malware Detection with Supervised Machine Learning Algorithms

Machine Learning for OSX Malware Detection

Hybrid Analysis on Credit Card Fraud Detection Using Machine Learning Techniques

Mapping CKC Model Through NLP Modelling for APT Groups Reports

Ransomware Threat Detection: A Deep Learning Approach

Scalable Fair Clustering Algorithm for Internet of Things Malware Classification

ISBN: 978-3-030-74753-4

Publisher: Springer Cham

Year of Publication: 2022

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Handbook of Big Data Analytics and Forensics - Research Book

Hot Research Book in Handbook of Big Data Analytics and Forensics

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