CyberGAN: Generating High-Fidelity Cybersecurity Data with Generative Adversarial Networks (GANs)

dc.contributor.author	Gonik, Julia
dc.contributor.author	Le, Joie
dc.contributor.author	Zhang, Yuening
dc.contributor.author	Viswanathan, Arun A
dc.date.accessioned	2022-02-03T22:47:59Z
dc.date.available	2022-02-03T22:47:59Z
dc.date.issued	2020-11-16
dc.identifier.citation	ASCEND (AIAA) 2020, Las Vegas, Nevada, November 16-18, 2020
dc.identifier.clearanceno	CL#20-4596
dc.identifier.uri	http://hdl.handle.net/2014/53522
dc.description.sponsorship	NASA/JPL	en_US
dc.language.iso	en_US
dc.publisher	Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2020
dc.title	CyberGAN: Generating High-Fidelity Cybersecurity Data with Generative Adversarial Networks (GANs)
dc.type	Presentation