Project Information
- Category: Deep Learning
- Paper: Research Paper
Description
Human-generated passwords are particularly susceptible to guessing attacks as a consequence of the limitation of precise recall and hence are not random. In this project, we make use of the excellent expressive power of sequence modeling neural networks such as LSTMs and GRUs to effectively guess passwords as compared to other cutting-edge password guessing techniques like Markov models, JtR, and PCFGs.
- Models Used:
- Embedding Model
- Long Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Generative Adversarial Network (GAN)