Detecting Phishing Patterns with Python: Open Source Strategies for Security Researchers

Phishing remains one of the most prevalent cybersecurity threats, targeting individuals and organizations through deceptive emails, malicious URLs, and fake websites. Detecting these patterns early is crucial for mitigating risks and protecting sensitive data. In this post, we explore how security researchers can leverage Python, along with open source tools, to identify and analyze phishing patterns effectively.

Understanding Phishing Patterns

Phishing tactics often involve specific patterns in URLs, domain registration details, and email content. Recognizing these recurring elements helps in creating automated detection systems. Key features to analyze include:

• URL structure and entropy
• Domain age and registration info
• Presence of suspicious subdomains
• Use of certain keywords in email content

Setting Up the Environment

To get started, ensure you have Python 3.x installed and install the necessary libraries:

pip install requests beautifulsoup4 tldextract python-whois

These tools assist in fetching webpage data, parsing HTML content, extracting domain parts, and querying WHOIS information.

Fetching and Analyzing URLs

A foundational step involves analyzing URL characteristics. Here's a Python snippet that checks URL entropy and suspicious subdomains:

import requests
import tldextract
import hashlib

def analyze_url(url):
    try:
        response = requests.get(url, timeout=5)
        url_bytes = response.url.encode()
        entropy = -sum([url_bytes.count(c) / len(url_bytes) * (len(url_bytes) / (url_bytes.count(c) + 1)) for c in set(url_bytes)])
        extract = tldextract.extract(url)
        subdomain_levels = extract.subdomain.split('.')
        is_suspicious_subdomain = len(subdomain_levels) > 3
        print(f"URL: {url}")
        print(f"Entropy: {entropy}")
        print(f"Suspicious subdomain: {is_suspicious_subdomain}")
    except Exception as e:
        print(f"Error analyzing URL: {e}")

# Example usage
analyze_url('http://login-verify-secure-abc.xyz')

This script assesses the complexity and subdomain suspicion level.

Domain Registration and Age

Another indicator involves analyzing the domain's age and registration details, which can suggest malicious intent. Using python-whois, we can fetch this info:

import whois
from datetime import datetime

def check_domain_age(domain):
    try:
        w = whois.whois(domain)
        creation_date = w.creation_date
        if isinstance(creation_date, list):
            creation_date = creation_date[0]
        age_days = (datetime.now() - creation_date).days
        print(f"Domain: {domain} | Age: {age_days} days")
        if age_days < 30:
            print("Warning: Domain is very new, which can be suspicious.")
    except Exception as e:
        print(f"Error fetching WHOIS data: {e}")

# Example usage
check_domain_age('xyz')

Domains with recent creation dates are common in phishing campaigns.

Content Analysis

Phishing emails often contain specific keywords or patterns. Using BeautifulSoup or regex, you can analyze email HTML or plain text:

import re

def analyze_email_content(content):
    suspicious_keywords = ['verify', 'urgent', 'password', 'update your account']
    for keyword in suspicious_keywords:
        if re.search(r"\b" + re.escape(keyword) + r"\b", content, re.IGNORECASE):
            print(f"Suspicious keyword detected: {keyword}")

# Example usage
sample_content = "Please verify your account immediately."
analyze_email_content(sample_content)

Integrating Detection Patterns

Combining multiple indicators – URL features, domain age, email content – allows for heuristic detection of phishing attempts. For example, flag a URL if it has high entropy, a new domain, and corresponds with suspicious email text.

Automation and Open Source Tools

Security researchers can automate this process, integrating each check into a pipeline. Open source projects like PhishDetect and frameworks like Scikit-learn can enhance detection with machine learning models trained on known phishing patterns.

Conclusion

Using Python and open source libraries, security researchers can develop robust systems for detecting phishing activities by analyzing URL structures, domain registration data, and email content. These strategies not only improve detection accuracy but also provide scalable solutions for ongoing monitoring.

Continued research and refinement, especially incorporating AI and data-driven models, will further enhance your defense mechanisms against evolving phishing threats.

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