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Original Article

RED: Resilient Environment for Defense Testing: An AI-Driven Adaptive Red Team Evaluation Framework

  • Paper ID 331
  • Volume 6, Issue 3
  • Pages 151-154
  • Published Online: May-June 2026
  • 32 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • Dr. B. M. Vidyavathi1
  • M. Akhil Sai2
  • M. Jahnavi3
  • M. Yashashwini Sai4
  • Maarif Maniyar5

1 Professor, Department of Artificial Intelligence and Machine Learning, Ballari Institute of Technology and Management, Bengaluru, Karnataka, India. 2 3 4 5 Student Department of Artificial Intelligence and Machine Learning, Ballari Institute of Technology and Management, Bengaluru, Karnataka, India.

Article Information

Published Online
May-June 2026
Pages
151-154
Article Type
Original Article
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References

1. S. H. Oh, J. Kim, and J. Park, ‘Dynamic Cyberattack Simulation: Integrating Improved Deep Reinforcement Learning with the MITRE
ATT&CK Framework,’ Sensors, Vol. 25, No. 13, 2025.
2. H. Rauf, M. Usama, F. Ali, and S. Iqbal, ‘Using Generative AI for Simulating Cyber Security Attacks and Defense Mechanisms,’ Wireless
Sensor Network, Vol. 17, 2024.
3. M. A. I. Mallick and R. Nath, ‘Simulating Cyber Threats: A Review of AI-powered Attack Simulators,’ Smart Energy and Sustainability,
Vol. 3, No. 2, 2025.
4. Y. Gao, ‘Cyber Attacks and Defense: AI Driven Approaches and Techniques,’ Electronics, Vol. 13, No. 15, 2024.
5. G. Apruzzese, M. Colajanni, and M. Marchetti, ‘Evaluating Cybersecurity Education and Training through Cyber Ranges,’ IEEE Security
& Privacy, Vol. 16, No. 6, 2018.
6. Y. Zhang, L. Wang, and T. Zhu, ‘Reinforcement Learning Based Attack Path Discovery in Network Security,’ IEEE Access, Vol. 8, 2020.
7. R. Mall, Software Project Management: Principles and Practices, Pearson Education, 2014.
8. I. Sommerville, Software Engineering: Concepts and Applications, Pearson Education, 2011.
9. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
10. C. J. C. H. Watkins and P. Dayan, ‘Q-learning,’ Machine Learning, Vol. 8, No. 3–4, pp. 279–292, 1992
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