Password Strength Checker is an application that checks how strong a password is. Some popular password strength meters use machine learning algorithms to predict the strength of your password. So, if you want to learn how to use machine learning to check your password’s strength, this article is for you. In this article, I will take you through how to create a password strength checker with machine learning using Python.
A password strength checker works by understanding the combination of digits, letters, and special symbols you use in your password. It is created by training a machine learning model on a labelled dataset of different combinations of letters and special symbols people use in passwords. The model learns from data about what combinations of letters and symbols can be classified as a solid or weak password. So to create an application to check the strength of passwords, we need to have a labelled dataset about different combinations of letters and symbols. I found a dataset on Kaggle to train a machine learning model to predict the strength of a password. We can use that data for this task. You can download the dataset from here. In the section below, I will take you through how to use Machine Learning to create a password strength checker using Python.
Let’s start by importing the necessary Python libraries and the dataset we need for creating a password strength checker: 1
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
data = pd.read_csv("data.csv", error_bad_lines=False)print(data.head())
password strength 0 kzde5577 1 1 kino3434 1 2 visi7k1yr 1 3 megzy123 1 4 lamborghin1 1
The dataset has two columns; password and strength. In the strength column:
Before moving forward, I will convert 0, 1, and 2 values in the strength column to weak, medium, and strong: 1
data = data.dropna()
data["strength"] = data["strength"].map({0: "Weak", 1: "Medium", 2: "Strong"})print(data.sample(5))
password strength 476676 xupet0n Weak 112569 cdm06690669 Medium 267402 bluerose1291 Medium 237407 2298409uur Medium 336018 jejien8 Weak
Now let’s move to train a machine learning model to predict the strength of the password. Before we start preparing the model, we need to tokenize the passwords as we need the model to learn from the combinations of digits, letters, and symbols to predict the password’s strength. So here’s how we can tokenize and split the data into training and test sets: 1
def word(password):
character=[]
for i in password:
character.append(i)
return character
x = np.array(data["password"])
y = np.array(data["strength"])
tdif = TfidfVectorizer(tokenizer=word)
x = tdif.fit_transform(x)
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.05, random_state=42)
Now here’s how to train a classification model to predict the strength of the password: 1
model = RandomForestClassifier()
model.fit(xtrain, ytrain)
print(model.score(xtest, ytest))
0.956991816498417
Now here’s how we can check the strength of a password using the trained model: 1
import getpass
user = getpass.getpass("Enter Password: ")data = tdif.transform([user]).toarray()
output = model.predict(data)
print(output)
Enter Password: ·········· ['Strong']
So this is how you can use machine learning to create a password’s strength checker using the Python programming language. A password strength checker works by understanding the combination of digits, letters, and special symbols you use in your password. I hope you liked this article on creating a password’s strength checker with Machine Learning using Python. Feel free to ask valuable questions in the comments section below.
