A Conceptual Cybersecurity Model Based on Generative Adversarial Networks: A Literature-Driven Approach

Abstract

The increasing reliance on digital platforms within university environments has contributed to a sharp rise in cybersecurity threats, necessitating more effective mechanisms for threat detection and mitigation. This study is a review of critical gaps in current cybersecurity frameworks, particularly in their ability to detect complex, evolving attack vectors in real-time. Comparative evaluation with existing approaches is expected to demonstrate improved accuracy in attack identification, reduced false-positive rates, and faster response times. In addressing the dynamic nature of cyber threats, this work also identifies future research directions, including the integration of reinforcement learning for autonomous adaptation and the incorporation of cross-network attack pattern analysis to support broader threat intelligence.