logo
Loading...
A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity
Computer ScienceElectronics

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.... show more
GeneralSummaryMetrics
Introduction
Literature Review
Methodology
Key Findings
Discussion
Conclusion
Limitations
Related Publications

Explore these studies to deepen your understanding

Adjacent work that informs or extends this paper's methodology and findings.

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.

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.

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 22+ 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