Detecting Phishing Patterns in Legacy Codebases with TypeScript

Introduction

In today’s cybersecurity landscape, phishing remains one of the most prevalent threats. As a Lead QA Engineer working within a legacy codebase, the challenge is to implement an effective pattern detection system capable of identifying potential phishing signals in URL structures, email contents, and user interactions. Leveraging TypeScript for this task offers strong typing, modularity, and integration ease, even within older systems.

Challenges of Legacy Codebases

Legacy systems often lack modern design principles, making it difficult to introduce new security features seamlessly. They may have minimal documentation, outdated dependencies, and tightly coupled modules. The goal is to build robust, maintainable detection logic that can integrate with existing workflows without requiring complete rewrites.

Approach Overview

The core idea is to develop a set of pattern-matching functions based on common phishing tactics, such as URL obfuscation, domain similarity, suspicious query parameters, and common social engineering tactics in email contents. TypeScript helps ensure stability through static typing, while modular architecture facilitates incremental integration.

Phishing Pattern Detection with TypeScript

Step 1: Define Rules and Patterns

Start by identifying known phishing patterns:

interface PhishingPattern {
  name: string;
  regex: RegExp;
  description: string;
}

const patterns: PhishingPattern[] = [
  {
    name: 'Suspicious URL Path',
    regex: /\badmin\b|\blogin\b|\bverify\b/i,
    description: 'URLs that contain common fake login paths'
  },
  {
    name: 'Domain Similarity',
    regex: /(.+)\.(com|net|org)$/,
    description: 'Check for look-alike domains'
  },
  {
    name: 'Obfuscated Email Content',
    regex: /(urgent|verify|password|security|click here)/i,
    description: 'Common urgent or suspicious keywords'
  }
];

Step 2: Pattern Matching Functions

Create functions to process URLs and email content:

function matchPatterns(input: string, patterns: PhishingPattern[]): string[] {
  const matches: string[] = [];
  for (const pattern of patterns) {
    if (pattern.regex.test(input)) {
      matches.push(pattern.name);
    }
  }
  return matches;
}

Step 3: Integration and Usage

In a legacy code context, you might process logs or form submissions:

const urlToCheck = 'http://secure-login.example.com/admin';
const emailContent = 'Please verify your account now!';

const urlMatches = matchPatterns(urlToCheck, patterns);
const emailMatches = matchPatterns(emailContent, patterns);

console.log('URL Pattern Matches:', urlMatches);
console.log('Email Content Matches:', emailMatches);

Step 4: Incremental Enhancement and Automation

Automate pattern checks within existing data pipelines for continuous monitoring. Over time, extend pattern rules using insights from new phishing trends, always maintaining TypeScript's type safety for maintainability.

Best Practices for Legacy Integration

• Use enums and interfaces to keep pattern definitions consistent.
• Modularize detection logic so it can be plugged into existing validation workflows.
• Log and alert on pattern matches without disrupting normal workflows.

Conclusion

Implementing phishing detection on legacy systems with TypeScript involves defining clear pattern rules, leveraging static typing for stability, and gradually integrating these checks into existing processes. This approach not only enhances security but does so in a maintainable and scalable way, keeping the system resilient against evolving phishing tactics.

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🛠️ QA Tip

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