Deep Learning-Driven Automated Data Generation for Enhanced Anomaly Detection in Cybersecurity

In anomaly detection mechanisms for cybersecurity, researchers heavily relay on accurate and comprehensive datasets. However, researchers face persistent challenges, including incomplete data, outdated datasets, and the absence of datasets tailored to specific, contemporary scenarios. These limitations impede the development of robust anomaly detection models and exacerbate the resource-intensive nature of data preparation and analysis. Widely used datasets, such as KDD Cup, are no longer sufficient due to their age and limited applicability to modern cybersecurity environments. To address these challenges, this paper proposes a novel hybrid approach combining automation and deep learning to automate synthetic data generation. Our methodology uses contemporary testing frameworks to ensure data validity and aligns with enterprise-specific requirements. By combining automated workflows with advanced deep learning models, this framework facilitates the creation of synthetic datasets that offer enhanced domain coverage and accuracy. Furthermore, it significantly reduces computational overhead and preparation time. This research contributes to the modernization of security data generation, offering a scalable and efficient solution to enhance anomaly detection mechanisms within the domain of cybersecurity.