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What is Generative Models for Synthetic Cyberattack Data about?

This document reviews the use of generative models, particularly Generative Adversarial Networks (GANs), in generating synthetic attack data for cybersecurity applications. It addresses the challenges of acquiring realistic datasets for training deep learning models due to privacy concerns and explores the effectiveness of GANs in producing diverse and realistic cyberattack data. The paper also evaluates the potential of synthetic data in enhancing the performance of intrusion detection systems against real

Author

lavanya744866

Language

EN