updated README

README.md

@@ -7,19 +7,28 @@ Given a vulnerability discovery procedure and a **large** enough number of train
 
 VDiscover aims to be used when there is a **large** amount of testcases to analyze using a **costly** vulnerability detection procedure. It can be trained to provide a quick prioritization of testcases. The extraction of features to perform a prediction is designed to be scalable. Nevertheless, this implementation is not particularly optimized so it should easy to improve the performance of it.
 
+## Requirements
+
+* Python 2.7
+* binutils
+* [python-ptrace](https://bitbucket.org/haypo/python-ptrace/)
+* [scikit-learn](http://scikit-learn.org/) for training/testing
+* [matplotlib](http://matplotlib.org/) for visualization (very experimental, optional)
+* [keras](http://keras.io/) for convolutional clustering (very experimental, optional)
+
+
 ## Quickstart
 
+It is recommended to manually install binutils, scikit-learn
+
     git clone https://github.com/CIFASIS/VDiscover.git
     cd VDiscover
     python setup.py install --user
 
-By default, the local installation of the command line utilities of VDiscover is performed inside ~/.local/bin, so it is recommended to add this directory into the PATH variable.
-This will also install the required python modules: [python-ptrace](https://bitbucket.org/haypo/python-ptrace/) for data collection and [scikit-learn](http://scikit-learn.org/) for training and prediction. Also [binutils](http://www.gnu.org/software/binutils/) is required.
-
-Our tool is composed by two components:
+By default, the local installation of the command line utilities of VDiscover is performed inside ~/.local/bin, so it is recommended to add this directory into the PATH variable. Our tool is composed by two main components:
 
-* **fextractor**: to extract dynamic and static features from testcases.
-* **vpredictor**: to train a new vulnerability prediction model or predict using a previously trained one.
+* **fextractor**: to extract dynamic and static features from test cases.
+* **vpredictor**: to train a new vulnerability prediction model or predict using a previously trained one. It can be used to cluster and visualize a set of test cases.
 
 Some examples of testcases of very popular programs (grep, gzip, bc, ..) can be found in  [examples/testcases](examples/testcases).  For example, to extract raw dynamic features from an execution of [bc](http://www.gnu.org/software/bc/):
 
@@ -29,7 +38,7 @@ And the resulted extracted features are:
 
     /usr/bin/bc	isatty:0=Num32B0 isatty:0=Num32B8 setvbuf:0=Ptr32 setvbuf:1=NPtr32 setvbuf:2=Num32B8 setvbuf:3=Num32B0 ...
 
-This raw data can be used to train a new vulnerability prediction model or predict using a previously trained one. Additionally, more detailed documentation is available [here](doc/index.md)
+This raw data can be used to train a new vulnerability prediction model or predict using a previously trained one. Additionally, more detailed (but outdated) documentation is available [here](doc/index.md).
 
 ## License