Model application with user activity ML filter for OWASP Night Tokyo 2016
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
static
templates
.gitignore
ML based detection of users anomaly activities (JPN).pptx
ML based detection of users anomaly activities (OWASP Night 9.3.2016).pptx
README.md
buzzboard.py
captcha.py
classifier.py
features.py
queues.py
recaptcha.py
requirements.txt
settings.py
setup.py
spamer.py

README.md

Buzzboard

Description

Model application with simple ML filter for OWASP Night Tokyo 2016

This is a special web application, which illustrates implementation of machine learning filters for users activity. Basically it is a Decision Tree classifier for HTTP requests features.

Installation

You need a Python 3.5 with ScikitLearn for this application. I'd recommend to install Anaconda IDE, because it has all libraries for this project (https://www.continuum.io/downloads).

Also as simple database this application needs Redis on localhost (with default parameters, take it on http://redis.io/download).

You also should define ReCAPTCHA API keys in settings.py file.