CryptoGenSec: A Hybrid Generative AI Algorithm for Dynamic Cryptographic Cyber Defence

As the world of cybersecurity constantly changes, traditional cryptographic techniques have faced limitations in the context of today's sophisticated and dynamic threats. Existing protections usually adopt static algorithms and key structures, making it difficult for them to resist the categories of modern attacks. This research paper, therefore, presents CryptoGenSec, a brand-new generative AI algorithm based on a hybrid consisting of generative adversarial networks (GANs) on reconnaissance learning (RL), for the purpose of increasing cryptographic cyber defences. CryptoGenSec applies a GAN to simulate various types of attack scenarios in cyberspace to perceive possible vulnerabilities. Then, RL refines the response strategies of our algorithm through recursive learning from the above simulations in real time and realizes the dynamic adaptation and evolution of defense mechanisms. By assessing the results of CryptoGenSec’s performance when traditional security methods are used as baselines, we can use several metrics for evaluation, such as detection accuracy, response time, resilience and evolution ability. According to these findings, the superiority of CryptoGenSec over convent