Confidential Generative Models for Cyber Security Training Environments

  • Authors

    • Dr. Farah Al-Farsi Department of Business Administration, Sultan Qaboos University, Muscat, Oman. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2019PI5Z7K

    Published 01-04-2019

  • Generative Models, Cybersecurity Training, Confidential Computing, Differential Privacy, Secure Machine Learning, GANs, LLMs In Cybersecurity, Threat Simulation, Adversarial Machine Learning, Privacy-Preserving AI

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    F. Al-Farsi, “Confidential Generative Models for Cyber Security Training Environments”, IJAIDT, vol. 2, no. 1, pp. 01–15, Jan. 2019, doi: 10.67228/30713315/IJAIDT-2019PI5Z7K.

  • Abstract

    As cybersecurity threats continue to evolve, the need for sophisticated training environments that simulate real-world attack scenarios has become paramount. Generative models, such as Generative Adversarial Networks (GANs) and Large Language Models (LLMs), offer immense potential to create dynamic, realistic, and adaptive training environments. However, the application of these models in cybersecurity contexts raises critical concerns about data confidentiality, model inversion attacks, and adversarial misuse. This paper explores the integration of confidential generative models—models that incorporate techniques such as differential privacy, secure multi-party computation, and homomorphic encryption—into cybersecurity training systems. We propose a secure framework for deploying generative models in cyber ranges and red team-blue team exercises, ensuring data integrity, user privacy, and robustness against model exploitation. We also present a case study and performance evaluation of our proposed system, offering insights into its practical feasibility and limitations.

  • References

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