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<p>The integration of Generative Adversarial Networks (GANs) with Genetic Algorithms (GAs) represents a novel approach to enhancing cryptographic methods, particularly in addressing challenges posed by quantum computing and increasingly sophisticated cyber threats. This research focuses on improving encryption strength, adaptability, and robustness against decryption attempts. By leveraging the optimization capabilities of GAs to evolve neural network architectures within a GAN framework, we significantly enhance the generator's ability to produce secure, quantum-resistant encryptions. The genetic algorithm optimized both the generator and discriminator networks over 300 generations, reducing generator loss from an initial 0.78 to a stable 0.65, while increasing discriminator loss, indicating improved encryption complexity. This study demonstrates the feasibility of using evolutionary techniques and adversarial training to create a dynamic, self-evolving cryptographic system, providing a foundation for future cryptographic innovations in quantum-resistant security. The methodology combines GA-driven network optimization and GAN-based adversarial training to address the challenges of quantum decryption and advanced adversarial attacks, setting new benchmarks for cryptographic security.</p>

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A GA-GAN approach for next-generation cryptographic security with a focus on quantum-resistant cryptography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月19日

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  • Purushottam Singh,
  • Prashant Pranav,
  • Sandip Dutta

摘要

The integration of Generative Adversarial Networks (GANs) with Genetic Algorithms (GAs) represents a novel approach to enhancing cryptographic methods, particularly in addressing challenges posed by quantum computing and increasingly sophisticated cyber threats. This research focuses on improving encryption strength, adaptability, and robustness against decryption attempts. By leveraging the optimization capabilities of GAs to evolve neural network architectures within a GAN framework, we significantly enhance the generator's ability to produce secure, quantum-resistant encryptions. The genetic algorithm optimized both the generator and discriminator networks over 300 generations, reducing generator loss from an initial 0.78 to a stable 0.65, while increasing discriminator loss, indicating improved encryption complexity. This study demonstrates the feasibility of using evolutionary techniques and adversarial training to create a dynamic, self-evolving cryptographic system, providing a foundation for future cryptographic innovations in quantum-resistant security. The methodology combines GA-driven network optimization and GAN-based adversarial training to address the challenges of quantum decryption and advanced adversarial attacks, setting new benchmarks for cryptographic security.