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phishing-detection

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Train a simple decision tree classifier to detect websites used for phishing

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dataset.csv
decision_tree.py
features.md
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logistic_regression.py
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# Detecting phishing websites using a decision tree

This repository is a tutorial explaining how to train a simple decision 
tree classifier to detect websites that are used for 
[phishing](https://en.wikipedia.org/wiki/Phishing). Typically, phishing
websites disguise as trustworthy websites in order to gain the trust
of their victims, and malicious parties use them to obtain sensitive
information from their victims: e.g., passwords or credit card numbers.
In this tutorial, we train a decision tree to detect such websites, with
a success rate of 90.5%.

## Installation

To get started, you should first clone this repository by running the 
following command from a `UNIX` terminal.

```
git clone https://github.com/npapernot/phishing-detection
```

This will download the code that trains the phishing detector, as well
as the training data required for that operation.

You should also install `scikit-learn`, which is a collection of tools
for machine learning written in Python. You can find instructions on how
to install it [here](http://scikit-learn.org/stable/install.html). On 
a `UNIX` machine configured 
with [`pip`](https://pypi.python.org/pypi/pip), the simplest way is to 
run:

```
pip install -U scikit-learn
```

Once you have installed `scikit-learn`, you can check whether the 
library is correctly setup by typing the following in a Python shell:

```
import sklearn
```

If the command runs with no error, you are ready to train the phishing
detector!

## Phishing Website Dataset

In this tutorial, we use a dataset of phishing website publicly
available on the [machine learning repository](https://archive.ics.uci.edu/ml/datasets/Phishing+Websites)
provided by UCI. You don't have to download the dataset yourself
as it is included directly in this repository (`dataset.csv` file) and 
was downloaded on your machine when you cloned this repository.

The dataset was collected by analyzing a collection of `2456` websites 
among which some were used for phishing and others not. For each website 
included in the dataset, `30` attributes are given. You can find a list
[here](https://github.com/npapernot/phishing-detection/blob/master/features.md).
The list includes for instance the URL length, whether the website
uses pop-up windows or Iframes, or how old the domain registration is.

Each website in the dataset is labeled by `-1` if it is not a phishing
website and by `1` if it is a website used for phishing.

## Training the decision tree to detect phishing website

You can find the code that trains the decision tree in the 
[`decision_tree.py`](https://github.com/npapernot/phishing-detection/blob/master/decision_tree.py) 
file. To run the code, simply execute it in a terminal:

```
python decision_tree.py
```

This will first train the decision tree on `2,000` websites, then use 
the trained model to predict whether `456` websites are used for 
phishing or not (these websites were not analyzed during training). 
The model should make predictions that are about 90.5% correct, i.e. the
accuracy of the model on the testing data should be 90.5%. Here is a
dump of the output made by the script.

```
Tutorial: Training a decision tree to detect phishing websites
Training data loaded.
Decision tree classifier created.
Beginning model training.
Model training completed.
Predictions on testing data computed.
The accuracy of your decision tree on testing data is: 0.906129210381
```

To understand how this was done, you can read the line by line comments 
in the [`decision_tree.py`](https://github.com/npapernot/phishing-detection/blob/master/decision_tree.py) file.

## What next?

You can try improving the accuracy of this simple classifier by changing
some of the default parameter values for the model. This is done by
modifying [this line](https://github.com/npapernot/phishing-detection/blob/master/decision_tree.py#L48) 
of `decision_tree.py`. To learn more about the 
parameters that you can define when calling `DecisionTreeClassifier()`,
take a look at the `scikit-learn` 
[documentation](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier).

You can also try different models like 
[Support Vector Machines](http://scikit-learn.org/stable/modules/svm.html) 
or [Neural Networks](http://scikit-learn.org/dev/modules/neural_networks_supervised.html). 
For instance, in [this file](https://github.com/npapernot/phishing-detection/blob/master/logistic_regression.py), the machine learning model is now a logistic
regression, but it performs worse than the decision tree.

## Questions or suggestions

If you have any questions or suggestions, feel free to send me an email
at [`nicolas@papernot.fr`](mailto:nicolas@papernot.fr).

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