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A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity

Computer Science

A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity

G. Agrawal, A. Kaur, et al.

Discover how deep learning can revolutionize cybersecurity through the exploration of synthetic data generation by Garima Agrawal, Amardeep Kaur, and Sowmya Myneni. This research delves into generative adversarial networks (GANs) and their potential in creating realistic cyberattack data, offering insights into training deep learning models while addressing privacy concerns.

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Playback language: English
Abstract
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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