Decipher

An 💻open-source tool for 🔓 cracking cipher-encrypted files.

Do you like the project? :)

Usage

An 💻 open-source tool for 🔓 cracking cipher-encrypted files.

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         display verbose-level information
  -g, --generate-dataset
                        generates the dataset for training
  -qt, --quick-train    quickly train the model by reducing complexity
  -t, --train-model     train the model (uses model_v1)
  -w PATH, --word-list PATH
                        specify the word list file, default is nltk.corpus
  -if PATH, --input-file PATH
                        specify the file 📄 to crack 🔑
  -i TEXT, --input TEXT
                        specify the text 🅰 to crack 🔑

Enjoy deciphering! 😄

Cracking

To decrypt a string. Use the --input to command to specify the input string.

python -m decipher --input "Encrypted text here"

python -m decipher -i "Encrypted text here"

To decrypt a text file. Use the --input-file to command to specify the file path.

python -m decipher --input-file test.txt

python -m decipher -if test.txt

Setup

If you don't have conda install Miniconda from here

git clone https://github.com/aswinmurali-io/decipher.git
conda env create -f environment.yaml
python -m decipher [args]

The tool is capable of generating the default dataset to get things started. But for this to work we need to use nltk dataset. To do this type the below code in the python interpreter.

import nltk
nltk.download()

Using custom word list

By default decipher will use nltk.corpus. You can specify different word file by passing the --word-list arg with a file name.

git clone https://github.com/dwyl/english-words
python -m decipher --generate-dataset --word-list english-words/words_alpha.txt

Training

To train the model for decipher. Use the --train argument. This will scan for dataset and start the training process.

python -m decipher --train

Speed up training

To speed up the training process use the --quick-train argument. The will reduce the training complexity to make the process faster. Useful for training large dataset with limited computational power.

python -m decipher --train --quick-train