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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.
This book covers a variety of topics that span from industry to
academics: hybrid AI model for IDS in IoT, intelligent
authentication framework for IoMT mobile devices for extracting
bioelectrical signals, security audit in terms of vulnerability
analysis to protect the electronic medical records in healthcare
system using AI, classification using CNN a multi-face recognition
attendance system with anti-spoofing capability, challenges in face
morphing attack detection, a dimensionality reduction and
feature-level fusion technique for morphing attack detection (MAD)
systems, findings and discussion on AI-assisted forensics,
challenges and open issues in the application of AI in forensics, a
terrorist computational model that uses Baum-Welch optimization to
improve the intelligence and predictive accuracy of the activities
of criminal elements, a novel method for detecting security
violations in IDSs, graphical-based city block distance algorithm
method for E-payment systems, image encryption, and AI methods in
ransomware mitigation and detection. It assists the reader in
exploring new research areas, wherein AI can be applied to offer
solutions through the contribution from researchers and academia.
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.
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