Generative AI for Cybersecurity: LLM-Driven Attack Dataset Augmentation in Web API Detection

As Web APIs become integral to modern digital ecosystems, their increasing complexity and widespread adoption have made them appealing targets for cyberattacks. Effective detection and prevention mechanisms are crucial, and machine learning (ML) techniques provide promising solutions for identifying anomalous API traffic and addressing emerging threats. However, the effectiveness of ML-based security models is limited by the scarcity of realistic, up-to-date datasets and the rapid obsolescence of static training data in the face of evolving attack methodologies. To address these challenges, we present an approach that employs Large Language Models (LLMs) to generate synthetic Web API attack datasets. By leveraging LLMs, researchers can access abundant, customizable, and domain-specific synthetic data that closely simulate contemporary traffic patterns. This eliminates privacy and security concerns associated with real-world datasets and ensures a continuously adaptable data source for training and refining ML-based Intrusion Detection Systems. Ultimately, this strategy enables the development of more resilient ML models capable of effectively countering the dynamic and sophisticated nature of modern Web API threats.