AI-Driven Cybersecurity and Threat Intelligence

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).

• 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.