AI-Driven Cybersecurity and Threat Intelligence : Cyber Automation, Intelligent Decision-Making and Explainability / by Iqbal H. Sarker.

Format:

Book

Author/Creator:

Sarker, Iqbal H., author.

Language:

English

Subjects (All):

Computer networks--Security measures.

Computer networks.

Artificial intelligence.

Machine learning.

Mobile and Network Security.

Artificial Intelligence.

Machine Learning.

Local Subjects:

Mobile and Network Security.

Artificial Intelligence.

Machine Learning.

Physical Description:

1 online resource (0 pages)

Edition:

1st ed. 2024.

Place of Publication:

Springer Nature 2024

Summary:

This book explores the dynamics of how AI (Artificial Intelligence) technology intersects with cybersecurity challenges and threat intelligence as they evolve. Integrating AI into cybersecurity not only offers enhanced defense mechanisms, but this book introduces a paradigm shift illustrating how one conceptualize, detect and mitigate cyber threats. An in-depth exploration of AI-driven solutions is presented, including machine learning algorithms, data science modeling, generative AI modeling, threat intelligence frameworks and Explainable AI (XAI) models. As a roadmap or comprehensive guide to leveraging AI/XAI to defend digital ecosystems against evolving cyber threats, this book provides insights, modeling, real-world applications and research issues. Throughout this journey, the authors discover innovation, challenges, and opportunities. It provides a holistic perspective on the transformative role of AI in securing the digital world. Overall, the use of AI can transform the way one detects, responds and defends against threats, by enabling proactive threat detection, rapid response and adaptive defense mechanisms. AI-driven cybersecurity systems excel at analyzing vast datasets rapidly, identifying patterns that indicate malicious activities, detecting threats in real time as well as conducting predictive analytics for proactive solution. Moreover, AI enhances the ability to detect anomalies, predict potential threats, and respond swiftly, preventing risks from escalated. As cyber threats become increasingly diverse and relentless, incorporating AI/XAI into cybersecurity is not just a choice, but a necessity for improving resilience and staying ahead of ever-changing threats. This book targets advanced-level students in computer science as a secondary textbook. Researchers and industry professionals working in various areas, such as Cyber AI, Explainable and Responsible AI, Human-AI Collaboration, Automation and Intelligent Systems, Adaptive and Robust Security Systems, Cybersecurity Data Science and Data-Driven Decision Making will also find this book useful as reference book.

Contents:

Part I Preliminaries

1. Introduction to AI-Driven Cybersecurity and Threat Intelligence

2. Cybersecurity Background Knowledge: Terminologies, Attack Frameworks and Security Life Cycle

Part II AI/XAI Methods and Emerging Technologies

3. Learning Technologies: Towards Machine Learning and Deep Learning for Cybersecurity

4. Detecting Anomalies and Multi-Attacks through Cyber Learning: An Experimental Analysis

5. Generative AI for Cybersecurity

6. Cybersecurity Data Science: Towards Advanced Analytics, Knowledge and Rule Discovery for Explainable AI Modeling

Part III Real-World Application Areas with Research Issues

7. AI-Enabled Cybersecurity for IoT and Smart City Applications

8. AI for Enhancing ICS/OT Cybersecurity

9. AI for Critical Infrastructure Protection and Resilience

10. CyberAI: A Comprehensive Summary of AI Variants, Explainable and Responsible AI for Cybersecurity.

Notes:

Includes bibliographical references.

ISBN:

3-031-54497-8