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atdisco

@DisCo is a graph based datastore designed to minimize reverse engineering efforts.

atis

Any Threat Intelligence to STIX (ATIS) autogenerates and enriches STIX bundles with data from open source threat intelligence sources.

baycal

Bayesian Model Calibration (BayCal) toolkit is a software plugin for Risk Analysis Virtual Environment (RAVEN) framework, arming at inversely quantifying the uncertainties associated with simulation model parameters based on available experiment data.

binarydriller

Binary Driller (BD) is a visualization tool which uses the data produced from the Troglodyte tool developed on the Deep Learning Malware project. Binary Driller performs function matching using the provided function embeddings (function representations), then displays the matches for each function in a layout that mimics the location of each function within the binary.

bindrill

Binary Driller (BD) is a visualization tool which uses the data produced from the Troglodyte tool developed on the Deep Learning Malware project. Binary Driller performs function matching using the provided function embeddings (function representations), then displays the matches for each function in a layout that mimics the location of each function within the binary.

blackbear

BlackBear is a MOOSE-based code for simulating degradation processes in concrete and other structural materials.

caldera_grid

caldera_icm

cape2stix

This software allows for the conversion, extraction, and transformation of malware behavior data from "Malware Configuration And Payload Extraction" (CAPEv2) sandbox reports, to Structured Threat Information eXpression (STIX). This allows for further analysis to be performed, sharing of threat data, and transit to a graph database.

cape2stixdata

Data supporting CAPE2STIX repository

carbon_dioxide

civet

Continuous Integration, Verification, Enhancement, and Testing

civet_example_recipes

Example recipes and scripts for use with CIVET

cmip

Charging Management and Infrastructure Planning (CMIP) model explore various charging infrastructure network designs to serve a free-floating car-sharing fleet and determine the charging downtime experienced by the fleet for each design. Development of the CMIP model had two major steps: (1) describing modeling assumptions and (2) developing an integer program (IP) that jointly optimizes decisions about locations to install DC fast chargers and EV-to-charger assignments. The CMIP model integrates an EV charging model, EV energy consumption model, and heterogeneous, real-world vehicle use data with an integer programming optimization model to identify optimal location of new charging stations and calculate vehicle downtime for charging. The CMIP model can be applied to understand: (a) the reduction of EV fleet downtime if an additional fast-charging station is added to the current infrastructure and (b) to what extent total vehicle downtime would be sensitive to additional charging infrastructure.

code_generator

The ns-3 code generator provides a way to automatically generate C++ simulation code for ns-3 from a high level network topology description.

conda-moose

A repository designed to hold the necessary formulas to build the MOOSE Environment packages designed for Conda distribution.

dart

The purpose of this code is to disassemble potentially malicious code into benign pieces that can safely be transported via any number of traditional methods without fear of infection.

deep-lynx

Deep Lynx is a unique data warehouse where users can provide a custom ontology and have their data stored under said ontology in a graph-like format. Deep Lynx is written in Node.js and Rust and is actively maintained.

deep-lynx-data-historian

This software is intended to facilitate the ingestion of data from some data historian into Deep Lynx. A data historian in this instance is any location where sensor and operational data from some live asset is gathered. The data can be either manual retrieved by this software or the data historian source can push to a listening endpoint provided by this software.

deep-lynx-js-sdk

This software is a JavaScript package that interacts with the Application Programming Interface (API) suite provided by Deep Lynx. A JavaScript or TypeScript codebase may import this package in order to have access to these methods for communicating with a Deep Lynx instance.

deep-lynx-machine-learning

The Deep Lynx Machine Learning (ML) Adapter is a generic adapter that programmatically runs the ML as continuous data is received. Then, Jupyter Notebooks can be customized according to the project for pre-processing the data, building the machine learning models, prediction analysis of incoming data using an existing model, and forecasting anomalies / failures of the physical asset.

deep-lynx-matlab

The Deep Lynx MATLAB Adapter is a Python application that connects the Deep Lynx data warehouse with any MATLAB simulation.

deep-lynx-moose

The Deep Lynx MOOSE Adapter connects the Deep Lynx data warehouse with any MOOSE executable. The Adapter can receive events from Deep Lynx and will take incoming data to format a template input file for the MOOSE executable. Returns from the MOOSE run are sent back to Deep Lynx for use by other applications.

deep-lynx-python-package

deep-lynx-rust_sdk

This software is a Rust package that interacts with the Application Programming Interface (API) suite provided by DeepLynx. A Rust codebase may import this package in order to have access to these methods for communicating with a DeepLynx instance.

deep-lynx-supervisory-control

deep-lynx-timeseries-loader

'deeplynx-timeseries-loader' is a library designed to make it as easy as possible for users to download and access timeseries or tabular data from DeepLynx.

deep-lynx-unc