2021 Machine Learning Security Evasion Competition

Last update: May 01, 2022

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)

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.

2021 Machine Learning Security Evasion Competition

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Overview

2021 Machine Learning Security Evasion Competition

IMPORTANT: OUR 2021 TRAINED MODEL CAN BE DOWNLOADED HERE.

Dates

Contents

Contributing

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Overview

2021 Machine Learning Security Evasion Competition

IMPORTANT: OUR 2021 TRAINED MODEL CAN BE DOWNLOADED HERE.

Dates

Contents

Contributing

Owner

Fabrício Ceschin

Forecast dynamically at scale with this unique package. pip install scalecast

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Gaussian Process Optimization using GPy

SynapseML - an open source library to simplify the creation of scalable machine learning pipelines

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A complete guide to start and improve in machine learning (ML)

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EbookMLCB - ebook Machine Learning cơ bản

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LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms

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