logo
ResearchBunny Logo
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.

00:00
00:00
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Can We Trust Synthetic Data in Medicine? A Scoping Review of Privacy and Utility Metrics
Medicine and Health

Can We Trust Synthetic Data in Medicine? A Scoping Review of Privacy and Utility Metrics

B. Kaabachi, J. Despraz, et al.

A framework for human evaluation of large language models in healthcare derived from literature review
Medicine and Health

A framework for human evaluation of large language models in healthcare derived from literature review

T. Y. C. Tam, S. Sivarajkumar, et al.

Can We Trust Synthetic Data in Medicine? A Scoping Review of Privacy and Utility Metrics
Medicine and Health

Can We Trust Synthetic Data in Medicine? A Scoping Review of Privacy and Utility Metrics

B. Kaabachi, J. Despraz, et al.

Validity of Scottish predictors of child obesity (age 12) for risk screening in mid-childhood: a secondary analysis of prospective cohort study data—with sensitivity analyses for settings without various routinely collected predictor variables
Medicine and Health

Validity of Scottish predictors of child obesity (age 12) for risk screening in mid-childhood: a secondary analysis of prospective cohort study data—with sensitivity analyses for settings without various routinely collected predictor variables

G. Carrillo-balam, L. Doi, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny