Mohammad Waseem

Posted on Feb 3

Detecting Phishing Patterns with Node.js and Open Source Tools

#security #node #phishing

Introduction

Phishing remains one of the most persistent and damaging cybersecurity threats. Attackers craft deceptive URLs and mimic legitimate websites to steal sensitive user information. As a security researcher, leveraging open source tools and Node.js to develop effective phishing pattern detection systems can significantly enhance an organization’s defense mechanisms.

In this article, we explore a practical approach to identifying phishing patterns by analyzing URLs and content, utilizing open source libraries like node-fetch, cheerio, and hematite. We’ll walk through setting up a Node.js environment, gathering and analyzing data, and implementing algorithms to detect common phishing indicators.

Setting Up the Environment

Start by initializing a Node.js project and installing dependencies:

npm init -y
npm install node-fetch cheerio hematite

node-fetch: for fetching web content
cheerio: for parsing HTML
hematite: for URL analysis and pattern matching

Gathering Data

A critical step is collecting URLs—both legitimate and malicious. For demonstration, we’ll use sample URLs. In real-world scenarios, use threat intelligence feeds or security datasets.

const fetch = require('node-fetch');
const cheerio = require('cheerio');
const hematite = require('hematite');

const sampleUrls = [
  'http://example.com',
  'http://phishingsite.com/login',
  'https://secure-login.fakebank.com',
  'http://goodwebsite.org'
];

Analyzing URLs for Phishing Patterns

Phishing URLs often share specific traits: high word entropy, subdomain anomalies, or suspicious TLDs.

function analyzeUrl(url) {
  const parsedUrl = new URL(url);
  const hostname = parsedUrl.hostname;
  const path = parsedUrl.pathname;
  const tld = hematite.tld(hostname);
  const subdomains = hematite.subdomains(hostname);
  const domain = hematite.domain(hostname);

  const suspiciousTLDs = ['xyz', 'top', 'ru', 'cc']; // common malware/hacking TLDs
  const isSuspiciousTLD = suspiciousTLDs.includes(tld);
  const isSubdomainAnomaly = subdomains.length > 2;

  return {
    hostname,
    domain,
    subdomains,
    tld,
    isSuspiciousTLD,
    isSubdomainAnomaly,
    url
  };
}

Fetching and Checking Content

Phishing sites may include obfuscated code or identical page structures.

async function fetchPageContent(url) {
  try {
    const response = await fetch(url);
    if (!response.ok) return null;
    const html = await response.text();
    const $ = cheerio.load(html);
    // Look for suspicious scripts or forms
    const scripts = $('script').length;
    const forms = $('form').length;
    return { html, scripts, forms };
  } catch (error) {
    console.error(`Error fetching ${url}:`, error.message);
    return null;
  }
}

Pattern Detection Logic

Combine URL analysis and content inspection to flag potential phishing sites.

async function detectPhishing(url) {
  const urlAnalysis = analyzeUrl(url);
  const contentAnalysis = await fetchPageContent(url);

  // Simple heuristic rules:
  const isFlagged = (
    urlAnalysis.isSuspiciousTLD ||
    urlAnalysis.isSubdomainAnomaly ||
    (contentAnalysis && contentAnalysis.scripts > 10) || // Excessive scripts
    (contentAnalysis && contentAnalysis.forms === 0) // Malicious pages often lack forms
  );
  return { url, isPhishing: isFlagged, analysis: urlAnalysis };
}

// Running the detection
sampleUrls.forEach(async (url) => {
  const result = await detectPhishing(url);
  console.log(result);
});

Conclusion

This approach demonstrates how open source Node.js tools can be integrated to develop a modular and scalable phishing detection system. For production, incorporate machine learning models, larger threat intelligence datasets, and advanced heuristics. The critical aspect is continuously updating detection patterns, analyzing new threats, and adapting detection strategies accordingly. Combining URL pattern analysis with content inspection forms a robust foundation for proactive security research.

Final Note

To enhance this system, consider integrating with threat intelligence APIs, applying behavior analysis, and utilizing machine learning algorithms for adaptive detection. Open source tools like phishing-db, abuse.ch feeds, and custom ML models trained on phishing datasets can further improve detection accuracy.

Author: [Your Name], Senior Security Researcher and Node.js Developer

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