This article suggests a novel method for protecting corporate cybersecurity systems from malevolent attacks, based on Capsule Networks (CapsNets). The enhancement of hierarchical feature learning by Capital Networks is a critical component of its capacity to differentiate between authentic and fraudulent data. Robust optimization techniques and adversarial training are implemented to develop a model. The training seeks to be more resilient and beneficial in a larger environment by introducing perturbations one capsule at a time. CapsNets executed an effective operation, achieving 95% accuracy and 97% precision. In terms of managing adversarial assaults, CapsNets outperform baseline models greatly. The proposed approach exhibits potential as an improved cybersecurity defense method, as a result of its exceptional resilience and precision. This study demonstrates the efficacy of CapsNets in improving cybersecurity and also offers a glimpse into the adversarial defenses used in enterprise machine learning applications.