135 citations · 395 across the 28 of their papers we have counts for
6 papers · 1 filter
Linking Threat Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations for Cyber Hunting
Erik Hemberg, Jonathan Kelly, Michal Shlapentokh-Rothman +4
Many public sources of cyber threat and vulnerability information exist to help defend cyber systems. This paper links MITRE's ATT&CK MATRIX of Tactics and Techniques, NIST's Commo…
Analyzing the Components of Distributed Coevolutionary GAN Training
Jamal Toutouh, Erik Hemberg, Una-May O'Reilly
Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maint…
Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters
Una-May O'Reilly, Jamal Toutouh, Marcos Pertierra +5
Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic…
Data Dieting in GAN Training
Jamal Toutouh, Una-May O'Reilly, Erik Hemberg
We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resour…
Parallel/distributed implementation of cellular training for generative adversarial neural networks
Emiliano Perez, Sergio Nesmachnow, Jamal Toutouh +2
Generative adversarial networks (GANs) are widely used to learn generative models. GANs consist of two networks, a generator and a discriminator, that apply adversarial learning to…
Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
Jamal Toutouh, Erik Hemberg, Una-May O'Reilly
Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training su…