AI-Driven Cybersecurity and Threat Intelligence

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 useof 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.

Table of Contents

Part I Preliminaries

1 Introduction to Al-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: Toward Machine Learning and Deep Learning for Cybersecurity

4 Detecting Anomalies and Multi-attacks Through Cyber Learning: An Experimental Analysis

5 Generat ive Al and Large Language Modeling in Cybersecurity

6 Cybersecurity Data Science: Toward Advanced Analytics, Knowledge, and Rule Discovery for Explainable Al Modeling

Part Ill Real-World Application Areas with Research Issues

7 Al-Enabled Cybersecurity for loT and Smart City Applications

8 Al for Enhancing ICS/OT Cybersecurity

9 Al for Crit ical Infrast ructure Protection and Resilience

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

About the Author

Dr. Iqbal H. Sarker received his Ph.D. in Computer Science from Swinburne University of Technology, Melbourne, Australia in 2018. Now he is working as a research fellow at Cybersecurity Cooperative Research Centre (CRC) in association with Security Research Institute, Edith Cowan University, Australia through academia-industry collaboration including CSIRO’s Data61. Before that he also worked as a faculty member of the department of computer science and engineering of Chittagong University of Engineering & Technology. His professional and research interests include Cybersecurity, AI/XAI-based Modeling, Machine/Deep Learning, Data Science and Behavioral Analytics, Data-Driven Decision-Making, Automation and Intelligent Systems, Digital Twin, IoT and Smart City Applications, Critical Infrastructure Security and Resilience. He has published 100+ Journal and Conference papers in various reputed venues published by Elsevier, Springer Nature, IEEE, ACM, Oxford University Press, etc. Moreover, he is a LEAD author of a research monograph BOOK titled “Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making”, published by Springer Nature, Switzerland, 2021. He has also been listed in the world’s TOP 2% of most-cited scientists in both categories [Career-long achievement & Single-year], published by Elsevier & Stanford University, USA. In addition to research work and publications, Dr. Sarker is also involved in a number of research engagement and leadership roles such as Journal editorial, international conference program committee (PC), student supervision, visiting scholar and national/international collaboration. He is a member of ACM, IEEE and Australian Information Security Association (AISA).