Cat Swarm Optimization to Cybersecurity and GANs-based Defence Solutions Enhancement.

This work explores the benefits of applying swarm optimization strategies to tackle cybersecurity challenges and to model both defense mechanisms and threat intelligence solutions—particularly those involving Generative AI, though not limited to them. Among the various swarm optimization algorithms, this study focuses on Cat Swarm Optimization (CSO). Specifically, CSO is employed to address a feature selection task, with the selected features subsequently used to train a Generative Adversarial Network (GAN) for cyber attack identification. CSO was chosen because the behavioral patterns of cats offer a compelling analogy for modeling the stealthy and dynamic nature of both cyberattacks and defensive responses. A case study, represented by a SQL-Injection attack, is also presented to provide a preliminary evaluation of the proposed approach.