Hcpy - Interface with Home Connect appliances in Python

Related tags

Deep Learninghcpy
Overview

dishwasher installed in a kitchen

Interface with Home Connect appliances in Python

This is a very, very beta interface for Bosch-Siemens Home Connect devices through their local network connection. It has some tools to find the TLS PSK (Pre-shared Key) that is used to allow local access, and a Python script that can construct the proper Websocket interface to subscribe to events.

WARNING: This is not ready for prime time!

The dishwasher has a local HTTPS port open (and the dryer seems to have unencrypted HTTP). Attempting to connect to the HTTPS port with curl results in a cryptic protocol error due to the non-standard cipher selection, ECDHE-PSK-CHACHA20-POLY1305. PSK also requires that both sides agree on a symetric key, so it is necessary to figure out what that key is before any further progress can be made.

Finding the PSK

application setup screen

You will need to set the dishwasher to "Local network only" in the setup application so that your phone will connect directly to it, rather than going through the cloud services.

You'll also need a rooted Android phone running frida-server and the find-psk.frida script. This will hook the callback from the OpenSSL library hcp::client_psk_callback that is called when OpenSSL has made a connection and now needs to establish the PSK.

frida --no-pause -f com.bshg.homeconnect.android.release -U -l find-psk.frida

It should start the Home Connect application and eventually print a message like:

psk callback hint 'HCCOM_Local_App'
psk 32 0x6ee63fb2f0
           0  1  2  3  4  5  6  7  8  9  A  B  C  D  E  F  0123456789ABCDEF
00000000  0e c8 1f d8 c6 49 fa d8 bc e7 fd 34 33 54 13 d4  .....I.....43T..
00000010  73 f9 2e 01 fc d8 26 80 49 89 4c 19 d7 2e cd cb  s.....&.I.L.....

Which gives you the 32-byte PSK value to copy into the hcpy program.

SSL logging

The Frida script will also dump all of the SSL traffic so that you can see different endpoints and things. Not much is documented yet.

Note that the TX from the phone on the websocket is "masked" with an repeating 4-byte XOR that is sent in the first part of each messages. The script could be augmented to decode those as well. The replies from the device are not masked so they can be read in the clear.

hcpy

The hcpy tool can contact your device, and if the PSK is correct, it will register for notification of events.

RX: {'sID': 2354590730, 'msgID': 3734589701, 'resource': '/ei/initialValues', 'version': 2, 'action': 'POST', 'data': [{'edMsgID': 3182729968}]}
TX: {"sID":2354590730,"msgID":3734589701,"resource":"/ei/initialValues","version":2,"action":"RESPONSE","data":[{"deviceType":"Application","deviceName":"py-hca","deviceID":"1234"}]}
TX: {"sID":2354590730,"msgID":3182729968,"resource":"/ci/services","version":1,"action":"GET"}
TX: {"sID":2354590730,"msgID":3182729969,"resource":"/iz/info","version":1,"action":"GET"}
TX: {"sID":2354590730,"msgID":3182729970,"resource":"/ei/deviceReady","version":2,"action":"NOTIFY"}
RX: {'sID': 2354590730, 'msgID': 3182729968, 'resource': '/ci/services', 'version': 1, 'action': 'RESPONSE', 'data': [{'service': 'ci', 'version': 3}, {'service': 'ei', 'version': 2}, {'service': 'iz', 'version': 1}, {'service': 'ni', 'version': 1}, {'service': 'ro', 'version': 1}]}
RX: {'sID': 2354590730, 'msgID': 3182729969, 'resource': '/iz/info', 'version': 1, 'action': 'RESPONSE', 'data': [{'deviceID': '....', 'eNumber': 'SX65EX56CN/11', 'brand': 'SIEMENS', 'vib': 'SX65EX56CN', 'mac': '....', 'haVersion': '1.4', 'swVersion': '3.2.10.20200911163726', 'hwVersion': '2.0.0.2', 'deviceType': 'Dishwasher', 'deviceInfo': '', 'customerIndex': '11', 'serialNumber': '....', 'fdString': '0201', 'shipSki': '....'}]}

Feature UID mapping

There are other things that can be hooked in the application to get the mappings of the uid to actual menu settings and XML files of the configuration parameters.

In the xml/ directory are some of the device descriptions and feature maps that the app downloads from the Home Connect servers. Note that the XML has unadorned hex, while the websocket messages are in decimal.

For instance, when the dishwasher door is closed and then re-opened, it sends the messages for 'uid':512, which is 0x020F hex:

RX: {... 'data': [{'uid': 527, 'value': 1}]}
RX: {... 'data': [{'uid': 527, 'value': 0}]}

In the xml/dishwasher-description.xml there is a statusList that says uid 0x020f is a readonly value that uses enum 0x0201:

">
    
  

In the xml/dishwasher-featuremap.xml there is a mapping of feature reference UIDs to names:

BSH.Common.Status.DoorState">
    
   
    BSH.Common.Status.DoorState
   

as well as mappings of enum ids to enum names and values:

Open Closed ">
    
   
      
    
     Open
    
      
    
     Closed
    
    
   
Owner
Trammell Hudson
I like to take things apart.
Trammell Hudson
Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

MRefG Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021 1. Requirements To reproduc

万理 5 Jul 26, 2022
Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

simplified_mediapipe_face_landmarks Extracts essential Mediapipe face landmarks and arranges them in a sequenced order. The default 478 Mediapipe face

Irfan 13 Oct 04, 2022
OpenGAN: Open-Set Recognition via Open Data Generation

OpenGAN: Open-Set Recognition via Open Data Generation ICCV 2021 (oral) Real-world machine learning systems need to analyze novel testing data that di

Shu Kong 90 Jan 06, 2023
This project deploys a yolo fastest model in the form of tflite on raspberry 3b+. The model is from another repository of mine called -Trash-Classification-Car

Deploy-yolo-fastest-tflite-on-raspberry 觉得有用的话可以顺手点个star嗷 这个项目将垃圾分类小车中的tflite模型移植到了树莓派3b+上面。 该项目主要是为了记录在树莓派部署yolo fastest tflite的流程 (之后有时间会尝试用C++部署来提升

7 Aug 16, 2022
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
LSUN Dataset Documentation and Demo Code

LSUN Please check LSUN webpage for more information about the dataset. Data Release All the images in one category are stored in one lmdb database fil

Fisher Yu 426 Jan 02, 2023
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Det

123 Jan 04, 2023
ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Voice2Series-Reprogramming Voice2Series: Reprogramming Acoustic Models for Time Series Classification International Conference on Machine Learning (IC

49 Jan 03, 2023
Winners of DrivenData's Overhead Geopose Challenge

Winners of DrivenData's Overhead Geopose Challenge

DrivenData 22 Aug 04, 2022
Interactive Terraform visualization. State and configuration explorer.

Rover - Terraform Visualizer Rover is a Terraform visualizer. In order to do this, Rover: generates a plan file and parses the configuration in the ro

Tu Nguyen 2.3k Jan 07, 2023
Generate high quality pictures. GAN. Generative Adversarial Networks

ESRGAN generate high quality pictures. GAN. Generative Adversarial Networks """ Super-resolution of CelebA using Generative Adversarial Networks. The

Lieon 1 Dec 14, 2021
Official implementation of DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations in TensorFlow 2

DreamerPro Official implementation of DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations in TensorFl

22 Nov 01, 2022
Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks

Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks Work accepted at NeurIPS'21 [paper, video]. If you use this code in

TU Delft 43 Dec 07, 2022
A large-scale database for graph representation learning

A large-scale database for graph representation learning

Scott Freitas 29 Nov 25, 2022
Deploy optimized transformer based models on Nvidia Triton server

🤗 Hugging Face Transformer submillisecond inference 🤯 and deployment on Nvidia Triton server Yes, you can perfom inference with transformer based mo

Lefebvre Sarrut Services 1.2k Jan 05, 2023
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings This is the repository for t

RegLab 39 Jan 07, 2023
Implementation of algorithms for continuous control (DDPG and NAF).

DEPRECATION This repository is deprecated and is no longer maintaned. Please see a more recent implementation of RL for continuous control at jax-sac.

Ilya Kostrikov 288 Dec 31, 2022
High-resolution networks and Segmentation Transformer for Semantic Segmentation

High-resolution networks and Segmentation Transformer for Semantic Segmentation Branches This is the implementation for HRNet + OCR. The PyTroch 1.1 v

HRNet 2.8k Jan 07, 2023
Pretraining Representations For Data-Efficient Reinforcement Learning

Pretraining Representations For Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Ch

Mila 40 Dec 11, 2022
A High-Quality Real Time Upscaler for Anime Video

Anime4K Anime4K is a set of open-source, high-quality real-time anime upscaling/denoising algorithms that can be implemented in any programming langua

15.7k Jan 06, 2023