Mohammad Waseem

Posted on Feb 3

Detecting Phishing Patterns Through Web Scraping on a Zero-Budget

#security #webscraping #phishing

Introduction

In the fight against cyber threats, phishing remains one of the most pervasive tactics used by malicious actors. As a Lead QA Engineer facing resource constraints, leveraging open-source tools like web scraping becomes a strategic asset to identify phishing patterns effectively without incurring additional costs.

This article explores a systematic approach to detect phishing URLs and related patterns using web scraping and pattern analysis with zero budget. We'll focus on practical implementation, highlighting Python's capabilities with requests and BeautifulSoup, along with strategies to identify suspicious features.

Understanding the Challenge

Phishing sites often mimic legitimate domains but have subtle differences—like homoglyphs, unusual URL structures, or hidden forms. Detecting these requires analyzing URL components, content, and contextual features. Traditional solutions might involve costly subscriptions to security feeds, but with open data and scrapers, we can develop our detection logic.

Setting Up the Environment

Begin with free, open-source Python libraries:

pip install requests beautifulsoup4

Web Scraping Strategy

Identify sources of phishing reports or suspicious sites. For demonstration, we use a publicly available repository or a domain list from repositories like PhishTank or APWG. Since this is a zero-budget approach, we'll rely on open data sources.

Here's an example of fetching a list of suspected domains:

import requests
from bs4 import BeautifulSoup

# Example URL of a public phishing report
url = 'https://example.com/phishing-sample-list'
response = requests.get(url)
if response.status_code == 200:
    soup = BeautifulSoup(response.text, 'html.parser')
    # Parsing logic depends on the page structure
    domains = [tag.text for tag in soup.find_all('td', class_='domain')]
    print(domains)

Ensure your scraper adapts to actual data sources relevant to your context.

Analyzing Phishing Patterns

Once data is collected, look for common features:

Homoglyphs or misspellings in domain names
Unusual URL lengths or components
Suspicious form actions or embedded scripts in page content
SSL certificates (lack or mismatch)

A sample analysis script:

import re

def is_suspicious_domain(domain):
    # Detect homoglyphs or common phishing patterns
    homoglyphs = {'0': 'O', '1': 'l', '@': 'a'}  # Simplified example
    for char in homoglyphs:
        if char in domain:
            return True
    return False

def analyze_urls(urls):
    for url in urls:
        if is_suspicious_domain(url):
            print(f"Suspicious domain detected: {url}")
        # Further pattern checks can be added here

# Example usage
analyze_urls(domains)

Leveraging Open Data for Continuous Detection

Set up periodic scraping schedules to keep threat intelligence current. Incorporate pattern rules based on the analysis, such as regex patterns to spot homoglyphs or common phishing URL structures.

For example, regex for detecting common URL anomalies:

phishing_pattern = re.compile(r"//[^/]+/[^/]+\.(php|html|asp|aspx)")
for url in domains:
    if phishing_pattern.search(url):
        print(f"Potential phishing URL: {url}")

Conclusion

Using free tools and open data sources, a Lead QA Engineer can build a robust, cost-effective system to detect phishing patterns. Continuous refinement of pattern rules and leveraging community reports are key to maintaining effectiveness.

This approach embodies a practical, scalable, zero-cost solution, emphasizing the importance of pattern recognition paired with vigilant data gathering. By systematically analyzing features and utilizing open data, organizations can proactively defend against phishing threats without financial investment.

Final Thoughts

While this method isn’t foolproof compared to commercial solutions, it empowers teams to start building their detection capabilities with minimal resources, fostering a security-first mindset across the organization.

🛠️ QA Tip

I rely on TempoMail USA to keep my test environments clean.

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