Cube Auth: 3x3x3 Cube Puzzle Authentication

PoC for Web Authentication based on Machine Learning analysis of 3x3 Rubik's Cube solving sequences. This repo includes the code for Authentication Backend Machine Learning component of the system.

Web Service Authentication Concept Test Service based on Machine Learning analysis of the movements of a 3x3x3 cube making use of the resolution sequences of cubes that can send both its rotation sequences and positioning through the BLE protocol is proposed. In order to make this possible, both a hardware development of the authentication device and software implementation of the platform, interface and authentication engine in the web service have been performed. In this work, a 3x3x3 cube has been designed and manufactured by ElevenPaths, called Cube11Paths. This device is capable of transmitting via BLE channel not only the sequences of turns, but also positioning sequences, which differentiates it from the rest of today’s commercial puzzles. To allow authentication in a Web Service using a 3x3x3 cube, a machine learning engine dedicated to binary classification is proposed through Logistic Regression, Support Vector Machine and Random Forest Classifier algorithms using the most representative characteristics of the resolutions of each user.

Alonso, J. M., et al. 2019; Rubika Cube Auth: 3x3x3 Cube Puzzle Authentication

See the publication related to this repository in the present link.

Project pipeline posts

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Installing

The software in this repo can be executed in Windows or Linux.

No special HW or SW prerequisites are required to execute all the routines that constitute the Backend.

The Python version used to develop and test this repo is Python 3.6.7

Installation of Anaconda Distribution

Anaconda Distribution was used to set up the Python environment used to created this component of the Cube Auth PoC. It can be deployed on Linux, Windows, and Mac OS X.

For Linux distro

Tested on Ubuntu Desktop 16.04 LTS and Ubuntu 18.04.1 LTS

Update the package lists for upgrades for packages that need upgrading, as well as new packages that have just come to the repositories and fetch new versions of packages existing on the machine.

$ sudo apt-get update
$ sudo apt-get upgrade

Our next step will be to download and install Anaconda Python 3.4

$ wget https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh
$ bash Anaconda3-4.2.0-Linux-x86_64.sh

Anaconda will incorporate automatically the routes to .bashrc file if specified during installation process. In case this didn't work, it can be manually added after installation procedure as indicated hereafter:

$ gedit ~/.bashrc

So the next line is added to the file

$ export PATH="/home/$USER/anaconda3/bin:$PATH"

For Windows OS

This software was tested in Windows 10.

Download the installer, double-click the .exe file and follow the instructions on the screen.

If you are unsure about any setting, accept the defaults. You can change them later.

When installation is finished, from the Start menu, open the Anaconda Prompt.

Building the virtual environment with Conda

Open a terminal and, with the provided environment.yml file, run:

$ conda env create -f environment.yml

A new virtual environment called cubeauth is created.

Activate the new environment

On Linux:

$ source activate cubeauth

On Windows:

$ activate cubeauth

New environment's correct installation cross-check

$ conda list

To verify that the copy was made:

$ conda info --envs

Install remaining dependencies

Within the cubeauth environment, run.

$ pip install -r ml_engine/requirements.txt

Training

Once the environment has been correctly installed, and just if the user has a suitable mongodb database to retrieve:

$ python ~/cube_auth/ml_engine/train.all.py

Firstly, this script will launch a Grid Search of hyperparameters for every user in the system which is authenticable based on the three supported algorithms for binary classification:

Logistic Regression;
Support Vector Classifier;
Random Forest Classifier;

Best hyperparamters are stored in ml_engine/logs in .txt files. Then the models are trained with the most suitable hyperparameters for each user and checkpoints are stored in ml_engine/checkpoints.

Training is also supported by receive.train.py when command for a certain user is received from Frontend.

Testing

Testing is very similar to receive.test.py when authentication attempt is indicated from Frontend for a certain authenticable user.

Built With

Python - Programming Language
Pika - Pika is a pure-Python implementation of the AMQP 0-9-1 protocol that tries to stay fairly independent of the underlying network support library. If you have not developed with Pika or RabbitMQ before, the Introduction to Pika documentation is a good place to get started. We reccomend version 0.11.0.

License

This project is licensed as MIT License - see the LICENSE.md file for details

Acknowledgments

Hat tip to anyone whose code was used

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

This software doesn't have a QA Process. This software is a Proof of Concept.

For more information please visit http://www.elevenpaths.com

eblancoh/cube_auth