AI Password Cracking - PassGAN AI Tool Download | Explore 2024 Best AI Security Tools Price, Review, Features And Alternative

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Pricing Type: Freemium (Free & Paid)
Price Start From($): 0
Operation Type: Open Source

Introduce of PassGAN

PassGAN is a generative adversarial network (GAN) architecture designed for password guessing. It is specifically tailored for the task of generating likely password candidates based on a given password policy or a set of leaked passwords. We called PassGAN AI Tool.

PassGAN leverages the power of deep learning to create realistic and diverse password samples that mimic the patterns and structures commonly found in real-world passwords.

What is PassGAN
How dose PassGAN work?
How to Protect Your Passwords? Advices by PassGAN AI Tool
How to use PassGAN AI Tool (Video)
Open Source Code of PassGAN
Features and Benefits of PassGAN
Official Website of PassGAN screenshot

AI Password Cracking - PassGAN AI Tool Download

Time It Takes Using AI to Crack Your Password [2023]

What is PassGAN?

PassGAN represents a concerning advancement in password cracking techniques. This latest approach uses Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, eliminating the need for manual password analysis. While this makes password cracking faster and more efficient, it is a serious threat to your online security.

PassGAN can generate multiple password properties and improve the quality of predicted passwords, making it easier for cybercriminals to crack your passwords and gain access to your personal data. As such, it is crucial to regularly update your passwords to protect yourself from this dangerous technology.

How does PassGAN work?

To understand how PassGAN works, examining the framework behind many modern password guessing tools is important. Typically, password guessing tools operate using simple data-driven techniques. This means they apply data models that run manual password analyses. In addition, the tools make further assumptions about password patterns and use password generation rules like concatenation.

Guessing passwords using such strategies is relatively efficient for small-scale and predictable passwords. However, when the sample size is large and complex password patterns are involved, these tools become either too slow or completely incapable of cracking the security codes. This is where systems lik PassGAN come into play.

PassGAN is a shortened version of the words “Password” and “Generative Adversarial Networks” (GAN). GAN is the general mechanism that runs this password-hacking tool. At its core, the mechanism runs on a neural network.

How to Protect Your Passwords? Advices by PassGAN AI Tool

Password strength is the main difference between an easy-to-hack password and a secure one. From the data obtained when we ran password samples on PassGAN, a digit-only password with ten characters can be instantly hacked.

A ten-letter password with only lowercase letters would take an hour to hack, while a ten-letter mixed-case password would take four weeks. On the other hand, a ten-character strong password using letters, symbols, and numbers would take five years to decipher.

This means the stronger your password, the lower the likelihood that people or AI systems can figure it out. Here’s a list of factors that ensure your password strength is difficult to compromise.

Use at least 15 characters.
Have at least two letters (upper and lower-case), numbers, and symbols in the password.
Avoid obvious password patterns, even if they have all the required character lengths and types.

How to use PassGAN AI Tool (Video)

Here are the specific and detailed operation methods and steps. You can watch the video and learn immediately. https://www.youtube.com/watch?v=DkXKxk3GTmI

Open Source Code of PassGAN

In Github, download Link is https://github.com/brannondorsey/PassGAN

This repository contains code for the PassGAN: A Deep Learning Approach for Password Guessing paper.

The model from PassGAN is taken from Improved Training of Wasserstein GANs and it is assumed that the authors of PassGAN used the improved_wgan_training tensorflow implementation in their work. For this reason, I have modified that reference implementation in this repository to make it easy to train (train.py) and sample (sample.py) from. This repo contributes:

Features and Benefits of PassGAN

Generator Network: The generator in PassGAN is responsible for creating new password candidates that are indistinguishable from real passwords. It learns the underlying patterns and characteristics of passwords from the training data and generates samples that adhere to these patterns.
Discriminator Network: The discriminator in PassGAN is trained to differentiate between real passwords and generated password candidates. It provides feedback to the generator, helping it improve its ability to create more realistic passwords over time.
Adversarial Training: PassGAN uses an adversarial training approach where the generator and discriminator networks compete against each other. This competition drives the generator to produce better password candidates while the discriminator learns to become more accurate in distinguishing real passwords from generated ones.
Password Policy Enforcement: PassGAN can be customized to adhere to specific password policies, such as minimum length, character constraints, and other rules commonly used in password creation. This ensures that the generated passwords meet the desired security requirements.
Evaluation Metrics: PassGAN performance can be evaluated using metrics such as password strength, diversity of generated passwords, and similarity to real passwords. These metrics help assess the effectiveness of the model in generating high-quality password candidates.