File size: 1,750 Bytes
fdad801 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score
import nltk
from nltk.corpus import stopwords
import re
# Download NLTK stopwords
nltk.download('stopwords')
# Load dataset
# Assuming you have a CSV file with 'url' and 'label' columns
data = pd.read_csv('malicious_phish.csv')
# Preprocess URLs
def preprocess_url(url):
url = re.sub(r"http\S+", "", url) # Remove http links
url = re.sub(r"\d+", "", url) # Remove digits
url = re.sub(r"\W", " ", url) # Remove non-word characters
url = url.lower() # Convert to lowercase
return url
data['url'] = data['url'].apply(preprocess_url)
# Split data into training and testing sets
X = data['url']
y = data['type']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Vectorize URLs using TF-IDF
vectorizer = TfidfVectorizer(stop_words=stopwords.words('english'))
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)
# Train a Naive Bayes classifier
model = MultinomialNB()
model.fit(X_train_tfidf, y_train)
# Predict and evaluate
y_pred = model.predict(X_test_tfidf)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")
# Function to predict if a URL is malicious
def predict_url(url):
processed_url = preprocess_url(url)
vectorized_url = vectorizer.transform([processed_url])
prediction = model.predict(vectorized_url)
return prediction[0]
# Example usage
print(predict_url("br-icloud.com.br"))
|