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Agentic Intelligence and the Rise of Autonomous Cyber Warfare - Couverture souple

 
9798260004142: Agentic Intelligence and the Rise of Autonomous Cyber Warfare

Synopsis

The emergence of agentic intelligence redefines the landscape of modern conflict in cyberspace. As these systems evolve, they enable faster, more adaptive, and less predictable forms of cyber operations, giving rise to autonomous cyber warfare. Unlike traditional attacks that rely on human direction, agentic systems identify vulnerabilities and adjust strategies in real time with minimal oversight. This shift raises questions about control, accountability, and escalation, while challenging existing frameworks for defense. Understanding the intersection of agentic intelligence and cyber warfare is essential for anticipating the risks and responsibilities associated with autonomous digital conflict. Agentic Intelligence and the Rise of Autonomous Cyber Warfare examines agent-based architectures, reinforcement learning, generative AI, and multi-agent systems applied to both cyber-attacks and cyber defense. It explores how autonomous AI reshapes security operations, enabling self-healing systems, AI-driven security operations centers (SOCs), and adaptive defense mechanisms in complex digital environments. This book covers topics such as anomaly detection, cybersecurity, and machine learning, and is a useful resource for engineers, security professionals, government officials, academicians, researchers, and scientists.

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À propos des auteurs

Bahaa Eddine Elbaghazaoui is a professor at ENSA Beni Mellal and a PhD holder in Computer Science and Artificial Intelligence. With extensive experience in software engineering, he specializes in full-stack development, microservices, AI integration, and cloud computing. As a project manager and trainer, he has led and contributed to innovative solutions in telecommunications, insurance, and government sectors. His research focuses on data profiling and predictive analytics, with numerous publications in high-impact journals. Passionate about technology and education, he actively mentors, reviews scientific work, and advances cutting-edge software solutions.

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.

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