Welcome to the NeuralPassShield repository! This project empowers you to create secure and random passwords with the aid of deep learning. Utilizing LSTM (Long Short-Term Memory) neural networks, this password generator takes password security to a new level, making it ideal for enhancing the protection of your online accounts.
In the age of digital security, passwords play a vital role in safeguarding your online presence. With cyber threats on the rise, it's crucial to have strong, random, and unique passwords for your various accounts. NeuralPassShield provides you with a solution that leverages LSTM neural networks to generate such passwords effortlessly.
Our password generator creates secure passwords by learning from a dataset of common passwords. The LSTM neural network is trained to predict the next character in a password, resulting in the generation of strong and unpredictable passwords.
Before you get started, ensure you have the following prerequisites installed on your system:
- Python
- TensorFlow
- Pandas
- Scikit-learn
- Keras
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Clone this repository to your local machine using Git:
git clone https://github.com/iamrealvinnu/NeuralPassShield.git
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Install the required Python libraries using pip:
pip install tensorflow pandas scikit-learn keras
To generate secure passwords, follow these steps:
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Run the script and enter the desired password length and the number of passwords you want to generate.
python password_generator.py
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The script will utilize the trained LSTM model to produce strong, random passwords, which will be displayed in the terminal.
You can customize the password generation process to suit your needs. Modify the characters and special characters used for password generation in the code. Additionally, you can adjust the training parameters, such as epochs and batch size, to fine-tune the model's performance.
The model incorporates early stopping, ensuring efficient training and preventing overfitting. The training process will stop if validation loss does not improve for a specified number of epochs.
The repository also provides functionality to measure the speed of password generation for different password lengths, helping you understand the model's efficiency.
We welcome contributions to enhance the capabilities of NeuralPassShield. If you have ideas for improvement or new features, please feel free to open an issue or create a pull request. Your input is highly valued.
This project is licensed under the MIT License - see the LICENSE file for details.