The use of deep learning in cybersecurity is hampered by the lack of large, real-world datasets for training. Privacy concerns prevent the sharing of organizational data, leading to the exploration of synthetic data generation. Generative adversarial networks (GANs) are a promising solution, but their effectiveness in creating realistic cyberattack data needs further investigation. This paper reviews generative learning, analyzes GANs' data generation capabilities, and examines the use of synthetic data in training deep learning models for cybersecurity applications.
Publisher
Electronics
Published On
Jan 01, 2024
Authors
Garima Agrawal, Amardeep Kaur, Sowmya Myneni
Tags
deep learning
cybersecurity
synthetic data
generative adversarial networks
data generation
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