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
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc

Microsoft 5.6k Jan 07, 2023
Extended Isolation Forest for Anomaly Detection

Table of contents Extended Isolation Forest Summary Motivation Isolation Forest Extension The Code Installation Requirements Use Citation Releases Ext

Sahand Hariri 377 Dec 18, 2022
Mortality risk prediction for COVID-19 patients using XGBoost models

Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b

1 Jan 19, 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
Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas.

Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas. Its objective is to ex

Taylor G Smith 54 Aug 20, 2022
A pure-python implementation of the UpSet suite of visualisation methods by Lex, Gehlenborg et al.

pyUpSet A pure-python implementation of the UpSet suite of visualisation methods by Lex, Gehlenborg et al. Contents Purpose How to install How it work

288 Jan 04, 2023
Client - 🔥 A tool for visualizing and tracking your machine learning experiments

Weights and Biases Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to produ

Weights & Biases 5.2k Jan 03, 2023
A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

Shane Smiskol 4 Jun 03, 2022
Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model

A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model (Random Forest Classifier Model ) that helps the user to identify whether someone is showing positive Covid sym

Priyansh Sharma 2 Oct 06, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy m

Robin 55 Dec 27, 2022
K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

K Means Algorithm What is K Means This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of pr

1 Nov 01, 2021
Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Sean Zahller 1 Feb 04, 2022
Decision Weights in Prospect Theory

Decision Weights in Prospect Theory It's clear that humans are irrational, but how irrational are they? After some research into behavourial economics

Cameron Davidson-Pilon 32 Nov 08, 2021
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 03, 2023
To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction. The challenge aims to adress the problems of medical imbalanced data classification.

Marwan Mashra 1 Jan 31, 2022
Regularization and Feature Selection in Least Squares Temporal Difference Learning

Regularization and Feature Selection in Least Squares Temporal Difference Learning Description This is Python implementations of Least Angle Regressio

Mina Parham 0 Jan 18, 2022
[DEPRECATED] Tensorflow wrapper for DataFrames on Apache Spark

TensorFrames (Deprecated) Note: TensorFrames is deprecated. You can use pandas UDF instead. Experimental TensorFlow binding for Scala and Apache Spark

Databricks 757 Dec 31, 2022
healthy and lesion models for learning based on the joint estimation of stochasticity and volatility

health-lesion-stovol healthy and lesion models for learning based on the joint estimation of stochasticity and volatility Reference please cite this p

5 Nov 01, 2022
Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

Oracle 95 Dec 28, 2022