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Computational Analysis of Cyber Risk Management by AI Coaching of Network DDoS Attack Activity in European Industries

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Aleksander Szymczak
Jan Treur
Peter H. M. P. Roelofsma
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the critical issue of Distributed Denial of Service (DDoS) attacks, which pose significant threats to modern industries by disrupting operations and causing substantial financial damage. The text explores how AI and machine learning can monitor network traffic patterns to detect unusual behavior in real-time, offering faster and more intelligent reactions than traditional systems. A key focus is the interaction between AI systems and human decision-making, emphasizing the importance of human factors such as coordination, awareness, and training in cyber risk management. The chapter introduces an adaptive network model that simulates how AI coaching can enhance human response to DDoS attacks, reducing the impact of cyber threats. The model includes an AI Coach that monitors and corrects human errors, demonstrating the potential of AI-driven support systems in real-world cybersecurity environments. The simulation results highlight the significant reduction in financial losses achieved through AI coaching, underscoring its effectiveness in mitigating cyber risks.
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previous chapter Shallow Transformers with Applications Towards Image and Text Classification
next chapter Learning to Defer with Scoring Functions
Title
Computational Analysis of Cyber Risk Management by AI Coaching of Network DDoS Attack Activity in European Industries
Authors
Aleksander Szymczak
Jan Treur
Peter H. M. P. Roelofsma
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-56-6

Electronic ISBN: 978-981-9549-57-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4957-3_4
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