Confluence Of Ai Machine And Deep Learning In Cyber Forensics
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Confluence of AI, Machine, and Deep Learning in Cyber Forensics
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.
AI-Enhanced Solutions for Sustainable Cybersecurity
The rapid advancement of technology brings with it unprecedented opportunities for innovation and connectivity. However, alongside these advancements, the threat of cybersecurity breaches looms larger than ever. Cybersecurity breaches pose a significant challenge for individuals, organizations, and societies at large. As interconnections between digital environments multiply, so do the avenues for malicious actors to exploit vulnerabilities, jeopardizing the integrity of data and infrastructure. The escalating issue of cybersecurity demands a proactive and sustainable solution. AI-Enhanced Solutions for Sustainable Cybersecurity is a groundbreaking and comprehensive exploration of how artificial intelligence (AI) can be leveraged to fortify cybersecurity defenses in an increasingly complex digital landscape. By delving into topics such as intrusion detection systems, authentication protocols, and IoT security, the editors provide a nuanced understanding of the challenges facing cybersecurity practitioners today.
New Approaches to Data Analytics and Internet of Things Through Digital Twin
Author: Periyaswami, Karthikeyan
language: en
Publisher: IGI Global
Release Date: 2022-09-30
Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.