Detecting Phishing Patterns with Python: An Open Source Approach for DevOps

#python #devops #cybersecurity

In today's cybersecurity landscape, phishing remains a persistent threat, leveraging social engineering to compromise systems. For DevOps teams and security professionals, early detection of phishing patterns is critical to mitigate risks. This post explores how you can leverage Python and open source tools to develop an effective proof-of-concept for detecting potential phishing URLs and emails.

Understanding the Challenge

Phishing detection involves identifying suspicious patterns, such as obfuscated URLs, matches with blacklisted domains, or characteristic characteristics in email content. Manual inspection is resource-intensive; hence, automating this process provides scalability.

Setting Up the Environment

We'll utilize Python, along with open source libraries like requests, BeautifulSoup, scikit-learn, and phishy. Additionally, we'll leverage publicly available threat intelligence feeds.

pip install requests beautifulsoup4 scikit-learn phishy

Gathering Data

Effective detection depends on quality datasets. For this example, consider using open threat intelligence feeds like PhishTank or Malware Domain List.

import requests

# Example: Fetching a list of malicious domains
phish_tank_url = "https://data.phishtank.com/data/online-valid.json"
domain_data = requests.get(phish_tank_url).json()
malicious_domains = {entry['url'] for entry in domain_data}

Pattern Analysis

Phishing URLs often contain specific patterns such as lengthy subdomains, URL obfuscation tricks, or confusing domain names. We'll build a feature extractor from URL strings.

from urllib.parse import urlparse

def extract_features(url):
    parsed = urlparse(url)
    features = {
        'length': len(url),
        'dot_count': url.count('.'),
        'subdomain_length': len(parsed.hostname.split('.')[0]) if parsed.hostname else 0,
        'has_https': int(url.startswith('https')),
        'is_in_blacklist': int(parsed.hostname in malicious_domains),
        'contains_at': int('@' in url),
        'url_entropy': -sum([p * (p.bit_length()) for p in [url.count(c)/len(url) for c in set(url)]])
    }
    return features

Applying Machine Learning

Using labeled data (benign and malicious URLs), we can train a classifier. Here’s a simplified example utilizing logistic regression.

from sklearn.linear_model import LogisticRegression
import numpy as np

# Example datasets
benign_urls = ["https://example.com", "https://secure-login.com"]
malicious_urls = ["http://phishingsite.com/login", "http://banking.fakewebsite.net"]

# Extract features
X = []
y = []
for url in benign_urls + malicious_urls:
    features = extract_features(url)
    X.append(list(features.values()))
    y.append(0 if url in benign_urls else 1)

# Train classifier
clf = LogisticRegression()
clf.fit(X, y)

# Predict new URLs
test_url = "http://login.yourbank.com"  # example
test_features = extract_features(test_url)
prediction = clf.predict([list(test_features.values())])
print(f"URL '{test_url}' is {'malicious' if prediction[0] == 1 else 'benign'}")

Integrating with Open Source Tools

Enhance detection by integrating with tools like YARA rules, or open-source threat intelligence platforms like MISP. Automate data ingestion and alerting for real-time detection.

Conclusion

This approach demonstrates how a DevOps specialist can leverage Python, open source datasets, and machine learning techniques to automate phishing pattern detection. This pipeline can be expanded and integrated into existing CI/CD processes or security operations centers to proactively monitor threats.

Continuous refinement and dataset updates are crucial to maintain accuracy. Combining dynamic analysis, regular threat feed updates, and behavior analysis will yield more reliable detection systems.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

DEV Community