Leveraging Web Scraping to Detect Phishing Patterns in Legacy Codebases

Detecting Phishing Patterns in Legacy Web Applications Using Web Scraping

Legacy systems often represent a significant security challenge, especially when it comes to identifying sophisticated phishing patterns embedded within outdated codebases. In this post, we'll explore how security researchers can leverage web scraping techniques combined with static and dynamic analysis to uncover potential phishing indicators, even in legacy environments.

The Challenge of Legacy Codebases

Many organizations maintain legacy web applications that, while crucial for operations, are riddled with outdated code, poor security practices, and hard-to-interpret patterns. Traditional signature-based detection methods often falter due to the lack of modern security integrations. Hence, a flexible approach like web scraping, which allows us to analyze web interfaces and underlying code, becomes invaluable.

Approach Overview

Our method involves programmatically extracting content, scripts, and network activity using web scraping tools. This data then feeds into analysis pipelines that look for patterns typical of phishing sites, such as suspicious URLs, form behaviors, or obfuscated scripts.

Step 1: Automated Content Extraction

Using Python's requests and BeautifulSoup, we can fetch and analyze web pages:

import requests
from bs4 import BeautifulSoup

def fetch_page(url):
    response = requests.get(url)
    response.raise_for_status()
    return response.text

def parse_html(html):
    soup = BeautifulSoup(html, 'html.parser')
    return soup

url = 'http://legacy-site.example.com'
html_content = fetch_page(url)
soup = parse_html(html_content)

# Extract all URLs on the page
links = [a['href'] for a in soup.find_all('a', href=True)]
print("Found links:", links)

This simple scraper lists all hyperlinks, which can be further analyzed for suspicious patterns.

Step 2: Script and Code Analysis

Legacy sites might embed scripts or inline code that serve malicious intents. Extracting inline scripts helps identify obfuscated code snippets.

scripts = soup.find_all('script')
for script in scripts:
    if script.string:
        print('Inline script found:', script.string[:100], '...')

Analyzing these scripts can highlight suspicious URL redirections, form manipulations, or hidden iframes typical in phishing pages.

Step 3: Detecting Phishing Patterns

Common phishing indicators include:

• Mismatched URLs or domain homoglyphs
• Hidden form fields or auto-submission scripts
• Use of obfuscation techniques such as base64 encoding or minified scripts
• Suspicious third-party hosts or external scripts

For example, we can verify the domain authenticity by comparing linked domains with known trusted sources. Here’s a pattern-matching example:

import tldextract

trusted_domains = ['trustedsecure.com', 'banking.com']

def check_domain(link):
    ext = tldextract.extract(link)
    domain = ext.domain + '.' + ext.suffix
    return domain in trusted_domains

suspicious_links = [link for link in links if not check_domain(link)]
print('Suspicious links:', suspicious_links)

Such checks help flag potential phishing URLs embedded in legacy pages.

Putting It All Together

Integrating web scraping with heuristic analysis enables security teams to automate the detection of phishing patterns efficiently. Regular scans across legacy codebases can reveal hidden malicious behaviors, allowing proactive mitigation.

Conclusion

While legacy systems present unique challenges, combining web scraping with targeted analysis offers a powerful approach for security researchers. This methodology not only uncovers obvious threats but also reveals subtle indicators that may otherwise go unnoticed.

Adopting these techniques ensures a more resilient security posture, empowering organizations to detect and neutralize phishing threats lurking within their legacy infrastructure.

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