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Found 22 papers, 8 papers with code
Evolving Code with A Large Language Model
Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently.
LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing
We present prompt engineering approaches for a plan-act-report loop for one action of a threat campaign and and a prompt chaining design that directs the sequential decision process of a multi-action campaign.
Evaluating Efficacy of Indoor Non-Pharmaceutical Interventions against COVID-19 Outbreaks with a Coupled Spatial-SIR Agent-Based Simulation Framework
Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen.
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence
Artificial Intelligence (AI) and Machine Learning (ML) algorithms can support the span of indicator-level, e. g. anomaly detection, to behavioral level cyber security modeling and inference.
Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks
Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial interactions.
Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021
Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society.
Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation
Scaling the cyber hunt problem poses several key technical challenges.
BRON -- Linking Attack Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations
Many public sources of cyber threat and vulnerability information exist to serve the defense of cyber systems.
Cryptography and Security
Analyzing the Components of Distributed Coevolutionary GAN Training
We investigate the impact on the performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods.
Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters
We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements.
Data Dieting in GAN Training
We investigate training Generative Adversarial Networks, GANs, with less data.
Parallel/distributed implementation of cellular training for generative adversarial neural networks
Generative adversarial networks (GANs) are widely used to learn generative models.
Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks.
Spatial Evolutionary Generative Adversarial Networks
We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid.
Using Detailed Access Trajectories for Learning Behavior Analysis
Student learning activity in MOOCs can be viewed from multiple perspectives.
Transfer Learning using Representation Learning in Massive Open Online Courses
It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term.
Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
GANs are difficult to train due to convergence pathologies such as mode and discriminator collapse.
On Visual Hallmarks of Robustness to Adversarial Malware
A central challenge of adversarial learning is to interpret the resulting hardened model.
Approximating Nash Equilibria for Black-Box Games: A Bayesian Optimization Approach
Game theory has emerged as a powerful framework for modeling a large range of multi-agent scenarios.
Computer Science and Game Theory
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
We are inspired by them to develop similar methods for the discrete, e. g. binary, domain which characterizes the features of malware.
Distributed Stratified Locality Sensitive Hashing for Critical Event Prediction in the Cloud
The availability of massive healthcare data repositories calls for efficient tools for data-driven medicine.
PonyGE2: Grammatical Evolution in Python
Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process.
