This tool demonstrates visualization of specially-formatted Nmap scan data. It runs by 3 components:
- An ElasticSearch server were the data is stored and queried
- A backend server using Flask that communicates with the ElasticSearch server and exposes API services to the web
- The frontend server which provides the Web UI that interacts with the user and communicate with the backend server
- A running ElasticSearch server that is accessible to the backend
- Python (Tested with 3.4.3), Ruby, Bash
- npm
- pip
First, we create a local copy by cloning repo:
git clone https://github.com/rp-pedraza/nsv.git
Then, we change the working directory to the local copy:
cd nsv
Then, we prepare data for importing. This will process data from the raw JSONified nmap scan files in data directory and save them to data.processed.
ruby process-data.rb
After that, we import the processed data to the ElasticSearch server. The default target server is localhost:9200, but it can be changed by opening import-processed-data.sh with a text editor and changing the value next to ELASTICSEARCH_ADDRESS=.
bash import-processed-data.sh
After importing data, we now set up the backend server. Change the working directory to backend:
cd backend
From here, we create an isolated environment through virtualenv.
If you don't have the tool installed in your system, you can install it with pip.
pip install virtualenv
Alternatively, you can have it installed for the current user only by adding the --user option. This would install the files in ~/.local.
You would have to add ~/.local/bin to the value of the PATH variable to be able to relatively execute the virtualenv binary.
pip install virtualenv --user
PATH=~/.local/bin:${PATH}
You can also make the modification of PATH permanent by adding [[ -f ~/.bashrc ]] && . ~/.bashrc to ~/.bash_profile and PATH=~/.local/bin:${PATH} to ~/.bashrc.
Once virtualenv is properly installed, we continue with the creation of the environment. Make sure you're in the backend directory before running the following command:
virtualenv venv
Then, we activate the environment.
source venv/bin/activate
You would notice a change in your shell prompt when done. It would be prefixed with venv.
At this point, we can now freely install the dependency modules without worrying about causing conflict with other setups.
The following command will install all modules listed in requirements.txt, which are required by the backend to run.
pip install -r requirements.txt
Note: For some reason the dependency package appdirs does not get installed. If that happens, just run pip install appdirs to install it, and then run pip install -r requirements.txt again.
And here, we finally run the backend. It would interact with the ElasticSearch server which is expected to be listening on localhost:9200. If you have it somewhere else, you can change the values in config.py.
python application.py
The server by default listens on http://localhost:5000.
Open another terminal and change the working directory to nsv/frontend.
cd /path/to/nsv/frontend
We then install bower and npm components required by the frontend.
bower install && npm install
Then, we create the server instance. This may also automatically open up your browser and redirect it to the web applicaton's home page.
PATH=$(npm bin):$PATH grunt serve
The server by default listens on http://localhost:9000.
We can also compile the frontend to make it more distributable by running:
PATH=$(npm bin):$PATH grunt build
This would create a compiled version of the site in the dist directory.
From there, we can run the simple Python server:
( cd dist && python -m http.server )
The server by default listens on 0.0.0.0, port 8000.
Please see docker/README.md.