Название: Digital Defence: Harnessing the Power of Artificial Intelligence for Cybersecurity and Digital ForensicsАвтор: Ahlad Kumar, Naveen Kumar Chaudhary, Apoorva S. Shastri, Mangal Singh, Anand J. Kulkarni
Издательство: CRC Press
Год: 2025
Страниц: 189
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB
This book aims to provide a comprehensive overview of the applications of Artificial Intelligence (AI) in the area of Cybersecurity and Digital Forensics. The various chapters of this book are written to explore how cutting‑edge technologies can be used to improve the detection, prevention, and investigation of cybercrime and help protect digital assets.
Digital Defence covers an overview of Deep Learning and AI techniques and their relevance to cybersecurity and digital forensics, discusses common cyber threats and vulnerabilities, and how Deep Learning and AI can detect and prevent them. It focuses on how Deep Learning/Artificial Learning techniques can be used for intrusion detection in networks and systems, analyze and classify malware, and identify potential sources of malware attacks. This book also explores AI’s role in digital forensics investigations, including data recovery, incident response and management, real‑time monitoring, automated response analysis, ethical and legal considerations, and visualization. By covering these topics, this book will provide a valuable resource for researchers, students, and cybersecurity and digital forensics professionals interested in learning about the latest advances in Deep Learning and AI techniques and their applications.
Chapter 1, “Artificial Intelligence for Cybersecurity—Fundamentals and Evaluation,” lays the foundation by exploring the core concepts of AI and its transformative role in threat detection and response mechanisms. It discusses how AI, particularly Machine Learning algorithms, can analyze vast datasets to identify potential security breaches, providing a proactive defense against emerging threats. By automating incident response, AI‑driven tools are also revolutionizing how organizations mitigate cyber risks, ensuring rapid, efficient reactions to incidents. This chapter sets the stage for deeper discussions on how AI technologies are reshaping the landscape of cybersecurity, enabling more adaptive and robust digital defenses. Each subsequent chapter builds on this knowledge, guiding readers through advanced AI applications in digital forensics, data protection, and future trends in the ever‑evolving world of cyber defense.
Chapter 2, “Predicting Tomorrow’s Threats: A Legal Framework for AI‑Based Predictive Analytics in Cybersecurity,” explores the delicate balance between the rapid advancements of AI in predicting cyber threats and the slower pace of legal regulation.
Chapter 3, “The Invisible Defence: Detecting Zero‑Day Threats with AI,” tackles one of the most formidable challenges in cybersecurity—zero‑day threats, which exploit unknown vulnerabilities. This chapter explores how AI provides an innovative and proactive defense against these hidden dangers, surpassing traditional methods that often struggle to detect them. By analyzing behavior patterns and identifying potential exploits, AI offers a dynamic approach to zero‑day threat detection, adapting to unforeseen challenges in real time. Furthermore, this chapter delves into the technical foundations of AI in cybersecurity, from machine learning algorithms to neural networks, while emphasizing the critical role of large datasets in training these systems.
Chapter 4, “Fusion of Deep Architectures in Intent‑Driven Networks for Intrusion Detection” explores the cutting‑edge integration of AI into Intent‑Based Networking (IBN), a modern approach to network management designed to fulfill specific business objectives. This chapter highlights the use of advanced AI models, including Random Forest Classifiers, ID‑convolutional neural networks, and hybrid architectures combining convolutional layers with Long short‑term memory (LSTM) networks, to detect and respond to security breaches.
Chapter 5, “An In‑depth Analysis of Intrusion Detection Systems with an Emphasis on Multi‑Access Edge Computing and Machine Learning,” addresses the growing complexity of network security in the age of the Internet of Things (IoT). As IoT applications expand, the volume of network data and computational demands increase, creating vulnerabilities in resource‑constrained IoT devices...
Скачать Digital Defence: Harnessing the Power of Artificial Intelligence for Cybersecurity and Digital Forensics