Confluence of AI, Machine, and Deep Learning in Cyber Forensics (Advances in Digital Crime, Forensics, and Cyber Terrorism (ADCFCT))

Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication. Title Page Copyright Page Book Series Table of Contents Detailed Table of Contents Preface Acknowledgment Chapter 1: A Comprehensive Perspective on Mobile Forensics Chapter 2: Applications of Machine Learning in Cyber Forensics Chapter 3: Machine Learning Forensics Chapter 4: Crucial Role of Data Analytics in the Prevention and Detection of Cyber Security Attacks Chapter 5: Deep Learning Approaches to Overcome Challenges in Forensics Chapter 6: Deep Learning-Based Malware Detection and Classification Chapter 7: Detecting Fake News Using Deep Learning and NLP Chapter 8: Impediments in Mobile Forensics Chapter 9: Use-Case of Blockchain in Cybercrime and Cyberattack Chapter 10: Motivational Quotes-Based Intelligent Insider Threat Prediction Model Chapter 11: Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges Chapter 12: Challenges in Developing Software in Today's Scenario Compilation of References About the Contributors Index "The book provides original research about cyber forensics and its relationship to Artificial Intelligence (AI) and presents the results of research and case studies that advance the practice and understanding of cyber forensics methods and techniques to support efficient and effective investigations. It covers a variety of topics, including forensic analysis, cloud forensics, forensics storage, mobile device forensics, forensic reporting, forensics tools, and more"-- Provided by publisher