Skip to content

Electronic Transactions Validator based on input data and Machine Learning

License

Notifications You must be signed in to change notification settings

onticsoluciones/nofraud

Repository files navigation

#NoFraud

Electronic Transactions Validator.

#Table of Contents

#Description

NoFraud is a federated multi-node electronic transactions validator. Uses Machine Learning to check transactions and give back if transaction is fraudulent or legitim. End users can configurate to which nodes can connect and params to check depending to your needs. The network nodes is configured as a close - federated and trusted system that uses SSL, TLS and certificate to exchange data. End users connect to those nodes with SSL and make API-Rest request to check if transtactions are legitim or fraudulents. If transaction is legitim, you can continue the proceess, if is fraudulent, system denied operation. Once the transaction is finished, the information is sent to the rest of the nodes to feed the machine learning and help make the global network more secure.

#Technology used

  • Machine Learning
  • Python
  • PHP (Core)
  • Docker
  • Tensor Flow
  • API-Rest
  • Dashing.io

#Features

  • Command Line
  • Magento Module
  • Configurable Plugin via Admin Panel
  • Multinode Connections
  • Monitoring Dashboard

#Configuration

In data/config.yml you declare plugins that NoFraud node will use, they will be processed from up to down priority

In data/database.sqlite you can add users as [id, username, password (bcrypt)] (default: admin/admin)

Refer to https://github.com/onticsoluciones/nofraud-sample-data for data and plugin samples

#Usage

Once running, NoFraud expose and API REST interface you can interact:

  • GET petition /capabilities => you get a JSON array with variables network can return an assessment about

  • POST petition /assessment => you send a JSON array (key => value) and get a % assessment (0-100) which 0 is the worst and 100 the best

Additionally you can send transactions for learning puporses adding "learn" variable set to 1 and "condition" set to 1 for a god transaction and 0 for a bad one

#Credits

#Contributors

About

Electronic Transactions Validator based on input data and Machine Learning

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •