Leveraging Open Source Tools to Detect Phishing Patterns through API Development

#api #security #phishing

Introduction

Detecting phishing patterns remains one of the persistent challenges in cybersecurity. Phishing attacks evolve rapidly, making static detection methods insufficient. As a security researcher and developer, leveraging open source tools to build scalable and adaptive detection APIs offers a powerful approach to combat these threats.

In this post, we explore how to create a pattern detection API that identifies phishing URLs and email patterns using open source tools such as Python, Flask, scikit-learn, and malicious URL datasets. Our goal is to develop a manageable, extensible, and effective API that can be integrated into larger security workflows.

Gathering Data and Features

The foundation of any pattern detection system is quality data. We start by collecting datasets of known malicious URLs, which are available from sources like PhishTank, OpenPhish, or Abuse.ch. For illustration, we assume the dataset contains URLs labeled benign or malicious.

import pandas as pd

# Load datasets
url_data = pd.read_csv('phishing_dataset.csv')

# Sample structure
# url_data.head()
# | url | label |
# | http://malicious.com/login | malicious |
# | https://safe-site.org | benign |

Features extracted include URL length, presence of IP addresses, entropy, and suspicious keywords. These features help machine learning models distinguish between safe and malicious URLs.

import re
import numpy as np

def extract_features(url):
    features = {}
    features['length'] = len(url)
    features['has_ip'] = 1 if re.search(r'\b(?:\d{1,3}\.){3}\d{1,3}\b', url) else 0
    features['entropy'] = -np.sum([p*np.log2(p) for p in np.histogram(list(url), bins=256, density=True)[0] if p > 0])
    features['suspicious_keywords'] = int(any(keyword in url for keyword in ['login', 'secure', 'update', 'bank']))
    return features

# Apply feature extraction
features_df = url_data['url'].apply(extract_features).apply(pd.Series)

Developing the Detection Model

Using scikit-learn, we train a classifier—such as RandomForest—to identify phishing patterns:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X = features_df
y = url_data['label'].apply(lambda x: 1 if x=='malicious' else 0)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

preds = model.predict(X_test)
print(classification_report(y_test, preds))

Building the API with Flask

Now, encapsulate the model into a REST API for real-time detection. Using Flask:

from flask import Flask, request, jsonify
import pickle

app = Flask(__name__)

# Save the trained model
pickle.dump(model, open('phishing_detector.pkl', 'wb'))

# Load the model
loaded_model = pickle.load(open('phishing_detector.pkl', 'rb'))

@app.route('/detect', methods=['POST'])
def detect_phishing():
    data = request.get_json()
    url = data.get('url')
    features = extract_features(url)
    feature_vector = pd.DataFrame([features])
    prediction = loaded_model.predict(feature_vector)[0]
    result = 'malicious' if prediction == 1 else 'benign'
    return jsonify({'result': result})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Deployment Considerations

For production deployment, containerize the API using Docker, enable HTTPS, and implement logging and alerting mechanisms. Regularly update the dataset and retrain the model as new phishing tactics emerge.

Conclusion

By combining open source tools and APIs, a security researcher can build an effective, scalable system for detecting phishing patterns. Such implementations facilitate rapid integration into security workflows, enabling proactive threat mitigation. Continued refinement and adaptive learning are essential to counter the ever-changing landscape of cyber threats.

This approach demonstrates the synergy of data science, API development, and open source resources in practical cybersecurity defense strategies.

🛠️ QA Tip

Pro Tip: Use TempoMail USA for generating disposable test accounts.

DEV Community