Detecting Phishing Patterns with TypeScript on a Zero-Budget

In the fight against cyber threats, phishing remains one of the most prevalent and damaging tactics used by malicious actors. As a Lead QA Engineer facing budget constraints, developing an effective, scalable solution for detecting phishing patterns might seem daunting — but with the right approach leveraging TypeScript, it’s achievable without additional financial resources.

Understanding the Challenge

Phishing detection involves identifying suspicious URLs, email structures, or content that mimic legitimate sites to deceive users. Traditional solutions often rely on expensive third-party APIs or sophisticated machine learning models requiring extensive infrastructure. However, a zero-budget approach necessitates relying solely on open-source tools, client-side processing, and heuristic analysis.

Strategy Overview

Our approach involves analyzing URL patterns and email content heuristically, implementing lightweight pattern detection algorithms directly in TypeScript. The focus is on pattern recognition, domain analysis, and content heuristics. This method allows for quick integration into existing systems and offers reasonable detection performance, especially when combined with continuous rule updates.

Step 1: URL Pattern Heuristics

Phishing URLs often share traits like the use of URL obfuscation, suspicious subdomains, or inconsistent domains. Implementing pattern checks can be done as follows:

function isSuspiciousUrl(url: string): boolean {
  const urlObj = new URL(url);
  const hostname = urlObj.hostname;
  // Check for suspicious subdomains
  if (hostname.split('.').length > 3) {
    return true;
  }
  // Check for URL encoding or obfuscation
  const encodedUrl = encodeURIComponent(url);
  if (encodedUrl !== url) {
    return true;
  }
  // Basic domain blacklist (use a curated list)
  const blacklistedDomains = ['login-secure.com', 'verify-accounts.net'];
  return blacklistedDomains.includes(hostname);
}

// Usage:
console.log(isSuspiciousUrl('http://login-secure.com/account?')); // true

This pattern check helps flag common deceptive URL tactics.

Step 2: Analyzing Email Content

Phishing emails often contain urgent language, misspellings, or suspicious links. Implement simple text heuristic checks:

function containsSuspiciousLanguage(emailBody: string): boolean {
  const suspiciousKeywords = ['urgent', 'verify now', 'immediate action', 'click here', 'password'];
  const lowerCaseBody = emailBody.toLowerCase();
  return suspiciousKeywords.some(keyword => lowerCaseBody.includes(keyword));
}

// Usage:
console.log(containsSuspiciousLanguage('Please verify your account immediately.')); // true

Coupled with URL checks, this heuristic improves detection accuracy.

Step 3: Combining Heuristics

To enhance detection, combine URL and content heuristics into a single assessment:

function isPotentialPhishing(url: string, emailBody: string): boolean {
  return isSuspiciousUrl(url) || containsSuspiciousLanguage(emailBody);
}

// Example:
console.log(isPotentialPhishing('http://account-verification.com', 'Your account verification is pending.')); // true

Final Thoughts

This zero-budget technique relies on common heuristic patterns, curated blacklists, and simple content analysis. While not foolproof, it offers a scalable, maintainable, and quick-to-deploy solution for phishing detection.

Furthermore, regular updates to heuristic rules and blacklists, along with integrating manual review processes, can improve effectiveness over time. For teams constrained by budget, open-source languages and environments like TypeScript enable rapid development and deployment, making cybersecurity tools accessible to organizations of all sizes.

Additional Recommendations

• Maintain and update a blacklist of suspicious domains.
• Incorporate user feedback to refine heuristics.
• Log suspicious detections for manual review.
• Consider integrating with open-source threat intelligence feeds if available.

By focusing on pattern recognition and heuristic analysis, organizations can create effective phishing detection systems without financial investments, securing their users and data efficiently.

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🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.