Details
AI-Driven Cybersecurity and Threat Intelligence
Cyber Automation, Intelligent Decision-Making and Explainability|
171,19 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 28.04.2024 |
| ISBN/EAN: | 9783031544972 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<div>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.</div><div><br></div><div>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. </div><div><br></div><div>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.</div>
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.
<b>Dr. Iqbal H. Sarker</b> 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).
<div>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.</div><div><br></div><div>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. </div><div><br></div><div>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.</div>
Presents comprehensive knowledge on AI-driven next generation cybersecurity modeling and applications Explores emerging technologies with AI variants such as classical AI, explainable AI, responsible AI, generative AI, machine learning, deep learning and data science towards cybersecurity applications Provides a holistic perspective on the transformative role of AI in securing the digital world through in-depth understanding, modeling, research challenges and future directions
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