2021 Machine Learning Security Evasion Competition

Related tags

Machine Learning2021-Machine-Learning-Security-Evasion-Competition
Overview

2021 Machine Learning Security Evasion Competition

This repository contains code samples for the 2021 Machine Learning Security Evasion Competition. Participants must register at https://mlsec.io and accept the terms of service in order to participate.

IMPORTANT: OUR 2021 TRAINED MODEL CAN BE DOWNLOADED HERE.

Dates

Challenge Start Date End Date
defender Jun 15, 2021 (AoE) Jul 23, 2021 (AoE)
attacker Aug 6, 2021 (AoE) Sep 17, 2021 (AoE)

*start and end times are Anywhere on Earth (AoE)

Contents

Outline the file contents of the repository. It helps users navigate the codebase, build configuration and any related assets.

File/folder Description
defender Sample source code for the defender challenge.
attacker Sample source code for the attacker challenge.
README.md This README file.
LICENSE The license for the sample code.
CODE_OF_CONDUCT.md Microsoft's open source code of conduct.
SECURITY.md Reporting security issues.

Contributing

This project welcomes contributions and suggestions, during or after the competition. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Owner
Fabrício Ceschin
Computer Science Master @ UFPR. PhD Student @ UFPR. Machine Learning & Cyber Security Scientist and Researcher.
Fabrício Ceschin
GitHub Repository
A library to generate synthetic time series data by easy-to-use factors and generator

timeseries-generator This repository consists of a python packages that generates synthetic time series dataset in a generic way (under /timeseries_ge

Nike Inc. 87 Dec 20, 2022
K-Means clusternig example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se

Emin 1 Dec 13, 2021
Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE)

FFT-accelerated Interpolation-based t-SNE (FIt-SNE) Introduction t-Stochastic Neighborhood Embedding (t-SNE) is a highly successful method for dimensi

Kluger Lab 547 Dec 21, 2022
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Horovod Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make dis

Horovod 12.9k Jan 07, 2023
ML-powered Loan-Marketer Customer Filtering Engine

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very length

Sagnik Roy 13 Jul 02, 2022
Classification based on Fuzzy Logic(C-Means).

CMeans_fuzzy Classification based on Fuzzy Logic(C-Means). Table of Contents About The Project Fuzzy CMeans Algorithm Built With Getting Started Insta

Armin Zolfaghari Daryani 3 Feb 08, 2022
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Data Science on AWS - O'Reilly Book Get the book on Amazon.com Book Outline Quick Start Workshop (4-hours) In this quick start hands-on workshop, you

Data Science on AWS 2.8k Jan 03, 2023
Falken provides developers with a service that allows them to train AI that can play their games

Falken provides developers with a service that allows them to train AI that can play their games. Unlike traditional RL frameworks that learn through rewards or batches of offline training, Falken is

Google Research 223 Jan 03, 2023
Create large-scale ML-driven multiscale simulation ensembles to study the interactions

MuMMI RAS v0.1 Released: Nov 16, 2021 MuMMI RAS is the application component of the MuMMI framework developed to create large-scale ML-driven multisca

4 Feb 16, 2022
GroundSeg Clustering Optimized Kdtree

ground seg and clustering based on kitti velodyne data, and a additional optimized kdtree for knn and radius nn search

2 Dec 02, 2021
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

Shigang Li 6 Jun 18, 2022
BigDL: Distributed Deep Learning Framework for Apache Spark

BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can w

4.1k Jan 09, 2023
Kaggle Competition using 15 numerical predictors to predict a continuous outcome.

Kaggle-Comp.-Data-Mining Kaggle Competition using 15 numerical predictors to predict a continuous outcome as part of a final project for a stats data

moisey alaev 1 Dec 28, 2021
Lightweight Machine Learning Experiment Logging 📖

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and co

Robert Lange 65 Dec 08, 2022
Bayesian optimization in JAX

Bayesian optimization in JAX

Predictive Intelligence Lab 26 May 11, 2022
A toolkit for making real world machine learning and data analysis applications in C++

dlib C++ library Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real worl

Davis E. King 11.6k Jan 02, 2023
This machine learning model was developed for House Prices

This machine learning model was developed for House Prices - Advanced Regression Techniques competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.

serhat_derya 1 Mar 02, 2022
A simple guide to MLOps through ZenML and its various integrations.

ZenBytes Join our Slack Community and become part of the ZenML family Give the main ZenML repo a GitHub star to show your love ZenBytes is a series of

ZenML 127 Dec 27, 2022
MLOps pipeline project using Amazon SageMaker Pipelines

This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drif

AWS Samples 3 Sep 16, 2022
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end

Najibulloh Asror 3 Feb 10, 2022