AI-Driven Cybersecurity Techniques, Systems, and Resilience
Language: English
Published by IGI GLOBAL SCIENTIFIC PUBLISHING, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - As cyber threats increase, AI strengthens cybersecurity defenses and enhances organizational resilience. AI-driven cybersecurity techniques leverage machine learning, predictive analytics, and automated threat detection to identify vulnerabilities, detect malicious activities, and respond to security incidents. These advanced systems enable organizations to protect critical digital assets, reduce response times, and improve the accuracy of threat intelligence in complex cyber environments. AI-driven cybersecurity promotes adaptive and resilient security frameworks that address emerging threats and safeguard information systems against future attacks. AI-Driven Cybersecurity Techniques, Systems, and Resilience explores the application of intelligent technologies in detecting, preventing, and responding to cyber threats. It examines AI-driven cybersecurity techniques, intelligent defense mechanisms, automated security systems, and resilience strategies that enhance an organization's ability to withstand, adapt to, and recover from cyberattacks. This book covers topics such as information systems, risk management, cybertechnology, and is a useful resource for engineers, security professionals, academicians, researchers, and scientists.…
Seller Inventory # 9798260034668
- Title
- AI-Driven Cybersecurity Techniques, Systems, and Resilience
- Author
- Tarik El Moudden
- Publisher
- IGI GLOBAL SCIENTIFIC PUBLISHING
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798260034668
- Item weight
- 663 grams
- Dimensions
- 254x178x19 mm
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Tarik El Moudden is a researcher and educator in the fields of artificial intelligence, deep learning, and geospatial data analysis. He earned his Ph.D. in Computer Science in 2025 from the Faculty of Sciences at Ibn Tofail University, Kenitra, with a dissertation titled “Advances in Deep Learning for Image Classification and Hypothesis Testing with Geospatial Data”. His research contributes to remote sensing applications, satellite image analysis, and predictive modeling using advanced deep learning techniques. Dr. El Moudden holds a DESA (Diplôme d'Études Supérieures Approfondies) in Computer Science and Telecommunications (2008) from the same university, with a focus on image indexing by content. He also earned a Bachelor's degree in Physics (2000), specializing in electronic physics, and holds an earlier certification in computer science education from the Regional Pedagogical Center of Rabat (2003). He has obtained numerous certifications from prestigious institutions, including NASA, IBM, Coursera, Packt, and HP Foundation, in topics such as machine learning, sports analytics, concurrent programming, project management with AI, and remote sensing applications. Dr. El Moudden has published widely in peer-reviewed Scopus-indexed journals, including Scientific African, International Journal of Electrical and Computer Engineering, and Journal of Geodynamics. His notable works explore slum detection using transfer learning, convolutional neural networks from scratch, and earthquake prediction via planetary alignment modeling. He has also co-authored papers on wastewater treatment optimization and federated learning in IoT environments. A frequent contributor to international conferences, Dr. El Moudden has presented innovative research on crop segmentation from Sentinel-2 imagery and on the relationship between comet activity and seismic events. He actively collaborates with other scholars and has proposed new strategies for slum upgrading using AI-based urban planning approaches. His interdisciplinary expertise, spanning deep learning, computer vision, remote sensing, and environmental data modeling, reflects a strong commitment to advancing scientific knowledge and its applications for sustainable development.
"About the title" may belong to another edition of this title.
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