Ethical Ransomware Simulation: A Safe Framework for Cybersecurity Training

Abstract

Ransomware is a major cybersecurity threat that causes huge financial and data losses. Most existing solutions focus on stopping ransomware after an attack, but there are not many safe ways to simulate and study ransomware in a controlled environment. This paper introduces an Ethical Ransomware Simulation Framework, a tool that allows cybersecurity students, researchers, and professionals to safely test and learn how ransomware works. The system includes custom ransomware creation, real-time monitoring, and testing of security defenses like firewalls and backups. It provides a risk-free, hands-on approach to understanding ransomware and improving protection methods. This framework helps users prepare for real-world ransomware attacks and strengthens overall cybersecurity.

Country : India

1 Desai Rohith Reddy2 K Yashwanth Kumar Reddy3 T Niranjan Babu

  1. Computer Science and Engineering (Cyber Security), Madanapalle Institute of Technology and Science, Andhra Pradesh, India
  2. Computer Science and Engineering (Cyber Security), Madanapalle Institute of Technology and Science, Andhra Pradesh, India
  3. Computer Science and Engineering (Cyber Security), Madanapalle Institute of Technology and Science, Andhra Pradesh, India

IRJIET, Volume 9, Special Issue of INSPIRE’25 April 2025 pp. 201-209

doi.org/10.47001/IRJIET/2025.INSPIRE33

References

  1. A.Alraizza And A. Algarni, “Ransomware Detection Using International Machine Learning,” Conference On Cybersecurity Technologies, Pp. 45-52, 2023.
  2. A.L. Narasimha Reddy Et Al., “Wannalaugh: A Configurable Ransomware Emulator—Learning To Mimic Malicious Storage Traces,” Arxiv Preprint Arxiv:2403.07540, 2024.
  3. C. Wang Et Al., “Leveraging Reinforcement Learning In Red Teaming For Advanced Ransomware Attack Simulations,” Arxiv Preprint Arxiv:2406.17576, 2024.
  4. C. Wang Et Al., “Reinforcement Learning For Ransomware Simulations,” Proceedings Of The Ieee International Conference On Artificial Intelligence And Security, Pp. 112-120, 2024.
  5. D. Diamantopoulos Et Al., “Wannalaugh: A Configurable Ransomware Emulator—Learning To Mimic Malicious Storage Traces,” Arxiv Preprint Arxiv:2403.07540, 2024.
  6. J. Von Der Assen Et Al., “Ransomai: Ai-Powered Ransomware For Stealthy Encryption,” Arxiv Preprint Arxiv:2306.15559, 2023.
  7. M. Ramaswamy, “Generative Ai For Ransomware Simulation,” International Workshop On Machine Learning In Cybersecurity, Pp. 33-41, 2024.
  8. M. Ramaswamy, “Generative Ai: Ransomware Attack Simulation And Workforce Education For It Enterprises And Small Businesses,” International Journal For Multidisciplinary Research, Vol. 6, No. 6, Pp. 123-130, 2024.
  9. M. Ramaswamy, “Generative Ai: Ransomware Attack Simulation And Workforce Education For It Enterprises And Small Businesses,” International Journal For Multidisciplinary Research, Vol. 6, No. 6, Pp. 123-130, 2024.
  10. S. S. S. R. DepuruAnd S. Devabhaktuni, “Ai Powered Ransomware Detection Framework,” Ieee International Conference On Information Security And Cryptology, Pp. 98-106, 2020.
  11. T. Xia Et Al., “Toward A Network-Assisted Approach For Effective Ransomware Detection,” Arxiv Preprint Arxiv:2008.12428, 2020.
  12. Y. Allon, “Ransomware Simulators—Reality Or A Bluff?,” Palo Alto Networks Blog, 2022.
  13. Z. Temechu And Z. Tadesse, “The Detection Of A Ransomware Attack On Iot Devices Deployed On Smart Home Networks,” Proceedings Of The Ieee International Conference On Internet Of Things, Pp. 78-85, 2023.