Enhancing AI Threat Modeling with Generative Adversarial Networks for Security Training Simulation Synthetic Dataset Creation
Keywords:
Generative Adversarial Networks, synthetic data, security simulations, AI threat modelingAbstract
Security-focused GANs produce realistic synthetic data. Security training simulations require realistic datasets for threat modeling and AI-based decision-making. Novelly, GANs contribute data to complete simulations. This project creates synthetic datasets for security system training and threat modeling accuracy using GANs. GANs, cybersecurity, dataset development, and simulations' security benefits are discussed. GANs may save companies money, build AI threat detection models, and simulate complex attack scenarios that are hard to replicate using data. The article claims GAN-generated synthetic data may improve AI-driven security models and training.
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