TrojAI cyber-network-c2-mar2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

The National Institute of Standards and Technology and the International Centre for Diffraction Data co-hosted a workshop on 17-18 October 2023 to identify and prioritize the goals, challenges, and opportunities for critical and emerging technology needs within industry, with an emphasis on leveraging artificial intelligence, data-driven methodologies, and high-throughput and automated workflows for accelerating x-ray-based structural analysis for materials development and manufacturing.

This dataset contains raw mechanical data (strain, stress and strain rate curvesand initial temperatures) for compression experiments performed on aluminum alloy 6061-T6using the NIST pulse-heated Kolsky Bar at strain rates between 2400 1/s and 2700 1/s and initialtemperatures from 23 °C to 527 °C and heating times ranging between 0.2 s and 3.5 s. At this time,the thermal history of the specimen during heating is not included in the dataset but this informationmay be included in a future update.

Round rl-randomized-lavaworld-aug2023-train Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

nlp-question-answering-aug2023-trainThis is the train data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Round rl-lavaworld-jul2023-train Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

TrojAI cyber-apk-nov2023 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of small feed forward multi-layer perceptron type neural network models classifying APK feature vectors as malware or clean. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

TrojAI cyber-network-c2-feb2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass filtered white Gaussian noise, 2) power law noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN), 3) generalized shot noise, 4) impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions.

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.