blackmode / waf-brain

Machine Learning WAF Based

WAF-Brain - the clever and efficient Firewall for the Web

What's WAF-Brain

This project was born to try to create a WAF (Web Application Firewall) with the Machine-Learning-Deep Learning Way.

WAF-Brain detect attacks by using Deep Learning Networks. It checks each parameter of each HTTP Request by the network. The Neural Network resolved if a specific parameter content is dangerous or not. If it consider that parameter is dangerous, then WAF-Brain will block the request.

Note

Currently the project only includes models for SQL Injection Attacks, but you can use your custom machine-learning model.

About the the research

You can find the complete document about the research process at RESEARCH.md

Install

$ pip install waf-brain

Usage

Demo App

We have developed a demo App, that you can find at demo app.

In summary, it exposes an end-point at /{tail} that accept a random parameter in tail.

For launching the App.

$ pip install aiohttp
$ python app.py
======== Running on http://127.0.0.1:5000 ========
(Press CTRL+C to quit)

Consume the App with curl is so easy:

$ curl -v /my-tail
OK

We we'll use this app to check the WAF

Launching WAF

The application that we want to protect listen at 127.0.0.1:5000. Then:

With the default model

$ waf_brain -A 127.0.0.1:5000 -l 0.0.0.0
======== Running on http://127.0.0.1:8000 ========
(Press CTRL+C to quit)

custom model

$ waf_brain -l 0.0.0.0 -A 127.0.0.1:5000 -M custom_model.h5
======== Running on http://127.0.0.1:8000 ========
(Press CTRL+C to quit)

Testing mode

For launch a server in test mode with our model on localhost, and collect partial results, launch this command

$ waf_brain -T --dump-file logs.txt -l 0.0.0.0 -A 127.0.0.1:5000
======== Running on http://127.0.0.1:8000 ========
(Press CTRL+C to quit)

Benchmarking

You have multiples kind of benchmarking, by a hacking tool (like sqlmap) or using our WAF-Benchmark.

In summary, in our test, we found that with WAF-Brain you can detect more attacks, in long payloads, than ModSecurity.

Other Options

CLI is self-explained you can use -h command to display all the options:

$ waf-brain -h
usage: waf-brain [-h] [-v] [--backend-timeout BACKEND_TIMEOUT]
                 [-A PROTECTED_URL] [-l LISTEN] [-p PORT] [-b BACKLOG]
                 [--blocking-mode] [--blocking-threshold BLOCKING_THRESHOLD]
                 [-M MODEL] [-T] [--dump-file DUMP_FILE] [-a]

WAF-brain: the clever and efficient Firewall for the Web

optional arguments:
  -h, --help            show this help message and exit
  -v                    log level

Server Options:
  --backend-timeout BACKEND_TIMEOUT
                        timeout to connect to the backend
  -A PROTECTED_URL, --protected-url PROTECTED_URL
                        address service to protect with the WAF
  -l LISTEN, --listen LISTEN
                        listen address. Default: 127.0.0.1
  -p PORT, --port PORT  listen port for service. Default: 8000
  -b BACKLOG, --backlog BACKLOG
                        maximum concurrent connections

WAF Behavior:
  --blocking-mode       enables active blocking of dangerous request
  --blocking-threshold BLOCKING_THRESHOLD
                        if the dangerous levels is upper this number, and
                        blocking mode is enabled, WAF will block a request
  -M MODEL, --model MODEL
                        model used for WAF

Enable testing mode:
  -T, --enable-testing  enable testing mode
  --dump-file DUMP_FILE
                        dump file to track each request
  -a, --access-log      enable access log for each request

Authors

Waf-Brain is being developed by BBVA-Labs Security team members

Waf-Brain is Open Source Software and available under the Apache 2 license

Contributions

Contributions are of course welcome. See CONTRIBUTING or skim existing tickets to see where you could help out.

Acknowledgments

Logo image was Designed by Freepik