AI Based Cybersecurity Management

Securing the Digital Economy
1. Auflage 2027
ISBN: 978-1-032-94169-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 256 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Advances in Cybersecurity Management

In our digitally driven world, the security of our financial systems, cryptocurrencies, and online economies is constantly under threat. AI Based Cybersecurity Management: Securing the Digital Economy provides a crucial guide to using cutting-edge artificial intelligence to defend against these modern cyber-attacks.

This essential volume brings together global experts to explore practical AI solutions for today's most pressing digital security challenges. Discover how to use AI for proactive threat detection, accurately forecast volatile cryptocurrency prices, and verify financial information to combat misinformation. The book also delves into securing blockchain transactions and protecting data with advanced cryptographic techniques, including defenses against future quantum computing threats.

Key features include:

• Leverage Generative AI and deep learning models like LSTM to predict and neutralize threats before they cause harm.
• Analyze market sentiment and build robust models to forecast prices and detect financial manipulation.
• Implement and enhance blockchain technology and post-quantum cryptography to secure digital transactions.
• Access practical strategies for risk management, regulatory compliance, and building resilient digital infrastructures.

Zielgruppe

Professional Practice & Development, Professional Reference, and Professional Training

Autoren/Hrsg.

Ahmad, Musheer

El-Latif, Ahmed A. Abd

Wani, Mudasir Ahmad

Maleh, Yassine

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Gebäudesicherheit
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Versicherungswirtschaft
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
• Geisteswissenschaften Architektur Gestaltung, Darstellung, Bautechnik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung

Inhaltsverzeichnis

1. From Reactive to Proactive: Leveraging Generative AI for Predictive Cybersecurity. 2. Machine Learning Approach to Cryptocurrency Price Prediction: Leveraging Social Media and Market Data. 3. Blockchain Technology for Securing Digital Economics. 4. Cryptographic Innovations for Securing the Digital Economy: Modern Techniques and their Impact. 5. Exploring the Relationship Between Bitcoin Price and Bitcoin Sentiment Index: An ARDL and SHAP Analysis. 6. AI-Driven Detection and Prevention of Financial Manipulation in Social Media. 7. Mitigating Cyber Risks in The Digital Age: A Guide to Cybersecurity Management. 8. Digital Economy Security: Challenges and Solutions. 9. Dual-Defense Framework Based on Cosine Similarity and Differential Privacy for Thwarting Poisoning Patterns in AI Federated Learning. 10. Leveraging Artificial Intelligence-Driven Deep Neural Networks to Thwart Adversaries in Web-Based Systems.

Über Autor(innen)

Musheer Ahmad is an Associate Professor in the Department of Computer Engineering at Jamia Millia Islamia, New Delhi, India. He received the B.Tech. and M.Tech. degrees from the Department of Computer Engineering, Aligarh Muslim University, India, in 2004 and 2008, respectively, and Ph.D from Jamia Millia Islamia. From 2007 to 2010, he has worked in the Department of Computer Engineering, Aligarh Muslim University, Aligarh. Dr. Musheer Ahmad has published over 160 research papers in internationally reputed refereed journals and conference proceedings. He has more than 6500 citations of his research works with an H-index of 45, i-10 index of 119, and cumulative JCR impact factor nearing 325. He has been consecutively listed among World's Top 2% researchers in studies conducted by Elsevier and Stanford University in 2021, 2022, 2023, 2024, and 2025. His research interests include multimedia security, chaos-based cryptography, cyber security, machine learning & deep learning, and optimization techniques. He has served as a reviewer and a technical program committee member of many international conferences. He is serving as associate editor of International Journal of Information Security and Privacy (IJISP), and International Journal of Artificial Intelligence in Scientific Disciplines (IJAISD) from IGI. He is Editorial Board member of Scientific Reports, and Discovery Computing. Recently, he is felicitated with Jamia Achievers Award by Jamia Millia Islamia, New Delhi.

Ahmed A. Abd El-Latif (Senior Member, IEEE) is a Professor of Computer Science specializing in Quantum Cybersecurity and AI-Driven Solutions, holding academic positions at several international universities. He earned his Ph.D. in 2013 from the Harbin Institute of Technology, China, where he also served as a Postdoctoral Fellow. Dr. Abd El-Latif has demonstrated extensive research leadership, spearheading and contributing to numerous national and international research projects. He has authored impactful publications in prestigious journals and conferences and has received multiple awards for his contributions. He has held key leadership roles in establishing research centers and advancing laboratory capabilities. Notably, he is the founder of the Center of Excellence in Quantum & Intelligent Computing (QIC), a premier research hub in the MENA region that integrates quantum educational platforms and post-quantum cybersecurity R&D initiatives. Prof. Abd El-Latif has held prestigious global roles, including Head of the MEGANET6G Lab in Russia, and Visiting Professorships at the Polish Academy of Sciences, Warsaw University of Technology, and Cracow University of Technology (Poland). An active contributor to the scientific community, he serves as Editor-in-Chief for several journals, including the International Journal of Information Security and Privacy (IJISP). He also acts as Chair/Co-Chair of international conferences and as Series Book Editor for publications such as Advances in Cybersecurity Management. His research spans quantum communications and cryptography, cybersecurity, the Artificial Intelligence of Things (AIoT), AI-based image processing, information hiding, and the application of dynamical systems (e.g., chaotic systems and quantum walks) in cybersecurity.

Yassine Maleh (Senior Member, IEEE) is an Associate Professor of Cybersecurity and IT governance at Sultan Moulay Slimane University, Morocco. He is the founding chair of IEEE Consultant Network Morocco and founding president of the African Research Center of Information Technology & Cybersecurity. Dr Maleh has made contributions in the fields of information security and privacy, Internet of things security, wireless and constrained networks security. His research interests include information security and privacy, Internet of things, networks security, information system, and IT governance. He has published more than 200 papers (in book chapters, international journals, and conference/workshop proceeding. He serves as Editor-in-Chief for the International Journal of Information Security and Privacy and International Journal of Smart Security Technologies, and as an Associate Editor for IEEE Access.

Mudasir Ahmad Wani is an Assistant Professor of Computer Science at Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia. He specializes in Natural Language Processing (NLP), Machine Learning, Deep Learning, Social Cybersecurity, and Cognitive Computing. His research focuses on the extraction and analysis of social data and the development of predictive models to address real-world challenges using advanced statistical, machine learning, deep learning, and NLP techniques.