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Found 34 papers, 11 papers with code
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
In this paper, we study the problem of constrained min-max optimization in a black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.
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.
CLAWSAT: Towards Both Robust and Accurate Code Models
We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models.
Representations of Computer Programs in the Human Brain
We find that the Multiple Demand system, a system of brain regions previously shown to respond to code, contains information about multiple specific code properties, as well as machine learned representations of code.
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.
Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation
Scaling the cyber hunt problem poses several key technical challenges.
Generating Adversarial Computer Programs using Optimized Obfuscations
We further show that our formulation is better at training models that are robust to adversarial attacks.
Signal Propagation in a Gradient-Based and Evolutionary Learning System
Generative adversarial networks (GANs) exhibit training pathologies that can lead to convergence-related degenerative behaviors, whereas spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e. g. Lipizzaner, are empirically robust to them.
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
STRATA: Simple, Gradient-Free Attacks for Models of Code
Neural networks are well-known to be vulnerable to imperceptible perturbations in the input, called adversarial examples, that result in misclassification.
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.
Sign Bits Are All You Need for Black-Box Attacks
Similar performance is observed on a standard IMAGENET model with an average of $579$ queries.
Dependency-Based Neural Representations for Classifying Lines of Programs
We investigate the problem of classifying a line of program as containing a vulnerability or not using machine learning.
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.
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.
Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks.
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.
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.
There are No Bit Parts for Sign Bits in Black-Box Attacks
We present a black-box adversarial attack algorithm which sets new state-of-the-art model evasion rates for query efficiency in the $\ell_\infty$ and $\ell_2$ metrics, where only loss-oracle access to the model is available.
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.
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate.
AST-Based Deep Learning for Detecting Malicious PowerShell
With the celebrated success of deep learning, some attempts to develop effective methods for detecting malicious PowerShell programs employ neural nets in a traditional natural language processing setup while others employ convolutional neural nets to detect obfuscated malicious commands at a character level.
Towards Distributed Coevolutionary GANs
Generative Adversarial Networks (GANs) have become one of the dominant methods for deep generative modeling.
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.
Likely to stop? Predicting Stopout in Massive Open Online Courses
Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks' data, the models attained AUCs of 0. 7.
