Detecting Phishing Patterns Using Web Scraping on a Zero-Budget

Phishing remains one of the most persistent threats in the digital landscape, often leveraging seemingly legitimate websites to deceive users. For security researchers operating under budget constraints, traditional solutions involving paid APIs or commercial tools can be inaccessible. Fortunately, web scraping provides a cost-effective method to identify patterns and unusual behaviors indicative of phishing. This post outlines a practical approach to developing a phishing detection system using open-source tools and free resources.

Understanding the Challenge

Phishing sites often mimic legitimate domains, contain suspicious patterns in URLs, or display content resembling trustworthy entities. Detecting these patterns involves analyzing site structure, content, and relationships between domain names. With web scraping, it's possible to gather this data from multiple sources and analyze it for common fraudulent traits.

Setting Up the Environment

For this solution, we'll leverage Python for its rich ecosystem of web scraping and data analysis libraries. The core tools include:

• Requests for HTTP requests
• BeautifulSoup for HTML parsing
• pandas for data analysis
• dnspython for domain analysis

You can install these packages via pip:

pip install requests beautifulsoup4 pandas dnspython

Data Collection Strategy

The key is to gather data from sources such as:

• Search engines (via Google or Bing APIs, or manual search results scraping)
• Blacklist or phishing report repositories
• Known suspicious URLs collected by user reports

For demonstration, let's focus on scraping search results from Bing (which can be done without API keys but with respect to their terms of service). You should customize scraping to avoid overloading search engines.

import requests
from bs4 import BeautifulSoup
import pandas as pd

def fetch_bing_results(query, num_results=10):
    headers = {'User-Agent': 'Mozilla/5.0'}
    results = []
    for start in range(0, num_results, 10):
        url = f'https://www.bing.com/search?q={query}&first={start}'
        response = requests.get(url, headers=headers)
        soup = BeautifulSoup(response.text, 'html.parser')
        for h2 in soup.find_all('h2'):
            a = h2.find('a')
            if a and a['href']:
                results.append(a['href'])
    return results

# Example usage
for url in fetch_bing_results('login facebook'): 
    print(url)

This script collects URLs related to a common phishing lure. Next, analyze these URLs for common phishy traits like subdomain patterns, lengthy query strings, or suspicious domain names.

Analyzing Domains for Phishing Indicators

Using dnspython, check domain age or DNS records for anomalies:

import dns.resolver
from datetime import datetime

def get_domain_info(domain):
    try:
        answers = dns.resolver.resolve(domain, 'SOA')
        for rdata in answers:
            serial = rdata.serial
            # Convert serial number to date if possible
            domain_age_days = (datetime.now() - datetime.fromtimestamp(serial)).days
            return {'serial': serial, 'age_days': domain_age_days}
    except Exception as e:
        return {'error': str(e)}

# Example domain analysis
print(get_domain_info('example.com'))

Domains with very recent registration date or low age are often suspect. Combining this with URL path analysis helps identify potential phishing sites.

Content-Based Pattern Identification

Download site content for keyword analysis or visual mimicry detection:

def fetch_site_content(url):
    try:
        response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, timeout=5)
        if response.status_code == 200:
            return response.text
    except requests.RequestException:
        return None

# Analyze for common phishing phrases
sample_url = 'http://suspicious-site.com'
content = fetch_site_content(sample_url)
if content and ('verify your account' in content.lower() or 'security alert' in content.lower()):
    print(f"Potential phishing content found in {sample_url}")

Combining Intelligence and Automation

By continuously scraping, analyzing domain info, and scanning website content, security teams can flag suspicious sites for manual review or automated blocking. The process can be orchestrated periodically with scripts scheduled via cron or similar tools.

Limitations and Ethical Considerations

• Always respect robots.txt files and search engine terms of use.
• Avoid aggressive scraping that may overload target sites.
• Use publicly available data to prevent privacy breaches.

Closing Remarks

While this approach doesn't replace commercial security solutions, it offers a feasible entry point for researchers and organizations with constrained budgets. Combining web scraping with basic domain and content analysis can significantly improve early detection of phishing campaigns, adding an essential layer of defense.

Through continuous refinement, including pattern detection algorithms and machine learning, such zero-cost solutions can evolve into powerful tools in the security arsenal.

References

• Bolton, R., & Hand, D. (2002). Statistical Fraud Detection: A Review. Statistical Science, 17(3), 235-255.
• Williams, A., & Lee, J. (2019). Phishing Detection Techniques. Journal of Cybersecurity & Digital Forensics.

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