Deep Learning for Introduction Detection Systems (IDS)
This repository is about using Deep Learning techniques to help build an introduction detection system.
INSTALLATION:
This repository has been tested with python3.6.5 via anaconda Install python 3.6 anaconda/miniconda following this link:
https://conda.io/docs/user-guide/install/index.html
Clone this repository (assuming you have git installed):
git clone https://github.com/mctrjalloh/IDS-deepLearning
Move into the downloaded repository for the rest of installations
Install dependencies:
After anaconda installation the conda command line should be available in the terminal
Use it to install pip
conda install pip
Then use pip to install package dependencies:
But first make a virtual environment:
conda create -n kddcup
Activate the virtual env:
source activate kddcup
Now install the dependencies:
pip install -r requirements.txt
(The requirements.txt file lives at the root of the downloaded repository)
Download the training and testing data for this project from this link:
http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
click on these links download the training and testing data:
kddcup.data_10_percent.gz A 10% subset. (2.1M; 75M Uncompressed) (training data)
corrected.gz (testing data)
kddcup.names (names file: data column names)
Move these files to a folder located in home:
cd ~
mkdir .kddcup
mv <downloaded files> .kddcup/
USAGE:
Create an alias for more convenience:
alias kddcup="python kddcup/main.py"
Now run:
kddcup train # to train a model
kddcup test # to test a model
kddcup predict # to classify a packet (not yet implemented)
kddcup plot # to plot some properties
You can also play around by importing objects and calling their methods
You can modify the config.json file to change some parameters of training and testing
config.json