QR2Pass-project - A proof of concept for an alternative (passwordless) authentication system to a web server

Overview

QR2Pass

This is a proof of concept for an alternative (passwordless) authentication system to a web server. The authentication is based on public key cryptographic challenges, that can correctly responded only by the owner of the private key. Challenges are presented in the form of a QR code which are scanned by the mobile app.

The project is based on the procedure proposed by the Snap2Pass paper, but not on the corresponding implementation. In contrast to Snap2Pass, it offers only public key authentication (i.e no shared secret) and there is no OpenID integration.

The server is written in Django and the client (mobile app) is written in Swift for the iOS platform

You can check an online version of the server here

Overview

During registration, user provides their public key to the server. For authentication, server presents a challenge (unique nonce that expires after 60 seconds). User needs to sign the challenge with their private key part. Server verifies the signature and if it's valid, user is authenticated into the web site.

The web app consists of 2 parts:

  • the core app that handles the web view (what users sees in their browser)
  • the api app that handles the out-of-band communication (to/from the mobile app)

Protocol overview

To complete the registration request, or to initate a login process, the web app (core) constructs QR codes that are scanned by the mobile app

register QR

the registration QR has the following info:

   {
       "version": Int, 
       "email": String, 
       "nonce": String,
       "provider": URL, 
       "respond_to": URL,
       "action": action enum //action.register 
   }
  • version: version of the prorocol (currently ignored)
  • email: the email provided in the registration form. It is currently used as a user identifier
  • nonce: a unique nonce (used to avoid replay attacks)
  • provider: base url for the site (this is the identifier for the site)
  • respond_to: where the client should send its response
  • action: either login or register (register in this case, duh!)

login QR

the login QR has a very similar schema:

    {
        "version": Int,
        "challenge": String,
        "validTill": Date, 
        "provider": URL, 
        "respond_to": URL,
        "action": action.login //action.login 
    }

email, is not provided by the server, but in the client's request (from the mobile app)

Out of band requests/responses

We define as out-of-band the requests between the mobile app and the server (api part) Browser - server (core part) is in-band

Registration

A user needs first to head to the registration page (in their browser) where they are asked for their email. If the email is valid and not already used, a registration QR code is presented (for 60 seconds). The user uses the mobile app to scan the QR code.
The app decodes the QR code (see register schema above) and extracts the URL from the "respond_to field"
If there is no registration data in the app for this site (defined by the "provider" field), it will then send a register request to this URL using the following schema:

    {
        "version": Int,
        "email": String,
        "public_key": String, 
        "nonce": String 
    }
  • version: version of the prorocol (currently ignored)
  • email: the user's email
  • public_key: the user's public key
  • nonce: the nonce offered by the server

Upon receiving the request, the server will perform the following checks:

  • request has the valid schema
  • the nonce received is a valid one and has not expired.
  • the nonce received, corresponds to the specific user.

If the checks are succesful, server creates a user in its DB and redirects the browser to login page

Server responds using the following schema (out-of-band):

    {
        "version": Int,
        "email": String,
        "status": String, 
        "response_text": String 
    }
  • status: "ok"/"nok"
  • response_text: a message showing more info about the status (e.g "invalid token")

Loging in

A previously registered user can head to the login page to log in. A QR is presented (for 60 seconds) The user uses the mobile app to scan the QR code.
The app decodes the QR code (see login schema above) and extracts the URL from the "respond_to field".
If there is registration data in the app for this site (defined by the "provider" field), it will then send a register request to this URL using the following schema:

{

    "version": Int,
    "username": String,
    "challenge": String, 
    "response": String 

}
  • username: the email of the user
  • challenge: the nonce provided by the server
  • response: the nonce signed by the private key of the user

Similarly to registration process, server will make some initial checks on the request (valid schema and nonce, etc). If the intial checks succeed, the signed challenge will be checked against the public key of the user (stored during the registration process). If all checks are succesful, user is authenticated in the backend and the browser will be redirected to the user page.

Server responds to the app with a repsonse using the same response schema as the in the registration process

Running the project

Client

The ios app doesn't use any external libraries and it is compatible to ios > 12.4
Keep in mind that iOS won't accept initiating unsecure connections (plain HTTP). See here for more information and ways to circumvent that, in case you want to test this locally.
Alternatively, you can use ngrok to map an external https endpoint to your local machine

Server

pre-requisites

The server uses redis for Django channels backend and for temporary storage (nonces), so you need to have redis running locally or remotely.
It also uses daphne as an asynchronous server. You can invoke daphne by running:

daphne qr2pass.asgi:application --port <PORT> --bind 0.0.0.0 -v2

but locally you can also use the usual runserver command:

python manage.py runserver

requirements

  • create a virtual environment
  • activate it
  • pip3 install -r requirements.txt

Settings

The default settings are defined in the settings/defaults.py file.
You need to fill in some additional settings corresponding to your deployment environment (see deployment-template.py) and define the DJANGO_SETTINGS_MODULE environmental variable for details) to point to your settings (see here)

A repository for generating stylized talking 3D and 3D face

style_avatar A repository for generating stylized talking 3D faces and 2D videos. This is the repository for paper Imitating Arbitrary Talking Style f

Haozhe Wu 191 Dec 22, 2022
Optimize Trading Strategies Using Freqtrade

Optimize trading strategy using Freqtrade Short demo on building, testing and optimizing a trading strategy using Freqtrade. The DevBootstrap YouTube

DevBootstrap 139 Jan 01, 2023
This repository contains the implementation of the paper Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans

Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans This repository contains the implementation of the pap

Photogrammetry & Robotics Bonn 40 Dec 01, 2022
Repository for the paper titled: "When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer"

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer This repository contains code for our paper titled "When is BERT M

Princeton Natural Language Processing 9 Dec 23, 2022
A collection of IPython notebooks covering various topics.

ipython-notebooks This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subje

John Wittenauer 2.6k Jan 01, 2023
The code uses SegFormer for Semantic Segmentation on Drone Dataset.

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

Dr. Sander Ali Khowaja 1 May 08, 2022
Conditional Gradients For The Approximately Vanishing Ideal

Conditional Gradients For The Approximately Vanishing Ideal Code for the paper: Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Appro

IOL Lab @ ZIB 0 May 25, 2022
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

CM-NAS Official Pytorch code of paper CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification in ICCV2021. Vis

JDAI-CV 40 Nov 25, 2022
💡 Type hints for Numpy

Type hints with dynamic checks for Numpy! (❒) Installation pip install nptyping (❒) Usage (❒) NDArray nptyping.NDArray lets you define the shape and

Ramon Hagenaars 377 Dec 28, 2022
Send text to girlfriend in the morning

Girlfriend Text Send text to girlfriend (or really anyone with a phone number) in the morning 1. Configure your settings in utils.py. phone_number = "

Paras Adhikary 199 Oct 25, 2022
Solution to the Weather4cast 2021 challenge

This code was used for the entry by the team "antfugue" for the Weather4cast 2021 Challenge. Below, you can find the instructions for generating predi

Jussi Leinonen 13 Jan 03, 2023
Official repository for CVPR21 paper "Deep Stable Learning for Out-Of-Distribution Generalization".

StableNet StableNet is a deep stable learning method for out-of-distribution generalization. This is the official repo for CVPR21 paper "Deep Stable L

120 Dec 28, 2022
Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 short.

Session-aware BERT4Rec Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 shor

Jamie J. Seol 22 Dec 13, 2022
Avalanche RL: an End-to-End Library for Continual Reinforcement Learning

Avalanche RL: an End-to-End Library for Continual Reinforcement Learning Avalanche Website | Getting Started | Examples | Tutorial | API Doc | Paper |

ContinualAI 43 Dec 24, 2022
Official implementation of "An Image is Worth 16x16 Words, What is a Video Worth?" (2021 paper)

An Image is Worth 16x16 Words, What is a Video Worth? paper Official PyTorch Implementation Gilad Sharir, Asaf Noy, Lihi Zelnik-Manor DAMO Academy, Al

213 Nov 12, 2022
This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation.

ISL This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation, which is accepted

19 May 04, 2022
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

Ruiqi Gao 39 Nov 10, 2022
This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization This codebase is the official implementation of Test-Time Classifier A

47 Dec 28, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022
Self-supervised learning optimally robust representations for domain generalization.

OptDom: Learning Optimal Representations for Domain Generalization This repository contains the official implementation for Optimal Representations fo

Yangjun Ruan 18 Aug 25, 2022