Building a Phishing Detection Dashboard with React: An Uncharted Approach

In the realm of cybersecurity, identifying phishing patterns promptly and accurately remains a pressing challenge. Recently, I stepped into the role of Lead QA Engineer tasked with developing a front-end solution that detects potential phishing URLs and patterns using React, despite the absence of formal documentation. This experience underscored the importance of adaptive problem-solving, rapid prototyping, and leveraging React's robust ecosystem for security-focused applications.

Initial Approach and Conceptualization

Faced with no documentation, the first step was to understand the core requirements: presenting suspicious URLs, categorizing them based on risk levels, and updating the detection logic dynamically. The UI needed to be intuitive, responsive, and capable of handling real-time data, making React an ideal choice due to its component-based architecture.

Setting Up the React Environment

We initiated the project with Create React App for a quick start:

npx create-react-app phishing-detection
cd phishing-detection

Since the detection logic was iterative and evolving, we focused on building flexible components and state management via React hooks.

Component Development

Key components included a URL Input Form, a Suspicious URL List, and a Risk Level Indicator.

// SuspiciousUrlList.jsx
import React from 'react';

function SuspiciousUrlList({ urls }) {
  return (
    <div>
      <h2>Suspicious URLs</h2>
      <ul>
        {urls.map((url, index) => (
          <li key={index} style={{ color: url.riskColor }}>
            {url.url} - {url.riskLevel}
          </li>
        ))}
      </ul>
    </div>
  );
}

export default SuspiciousUrlList;

The list dynamically displays URLs flagged as suspicious, color-coded by risk.

Implementing Detection Logic

Without documentation, our primary challenge was devising the detection algorithms. We utilized heuristic rules based on common phishing traits, like URL length, structural anomalies, or known malicious domains, which could be adjusted dynamically.

// Detection logic example
function detectPhishing(url) {
  const suspiciousDomains = ['malicious.com', 'phishy.org'];
  const isSuspiciousDomain = suspiciousDomains.some(domain => url.includes(domain));
  const isLongUrl = url.length > 75;
  // Simple heuristic rules
  if (isSuspiciousDomain || isLongUrl) {
    return { riskLevel: 'High', riskColor: 'red' };
  }
  return { riskLevel: 'Low', riskColor: 'green' };
}

These heuristics form the basis; they can be augmented with machine learning models or external threat feeds later.

Handling State and Data Flow

Managing suspicious URLs became straightforward with React's useState and useEffect. Users input URLs, trigger our detection function, and update the list.

// Main App.jsx
import React, { useState } from 'react';
import SuspiciousUrlList from './SuspiciousUrlList';

function App() {
  const [urls, setUrls] = useState([]);
  const [inputUrl, setInputUrl] = useState('');

  const handleAddUrl = () => {
    const detectionResult = detectPhishing(inputUrl);
    setUrls(prev => [...prev, { url: inputUrl, ...detectionResult }]);
    setInputUrl('');
  };

  return (
    <div>
      <input
        type="text"
        value={inputUrl}
        onChange={(e) => setInputUrl(e.target.value)}
        placeholder="Enter URL"
      />
      <button onClick={handleAddUrl}>Detect</button>
      <SuspiciousUrlList urls={urls} />
    </div>
  );
}

export default App;

Lessons Learned

Developing this feature without predefined documentation emphasized the need for rapid iteration, flexible component design, and heuristic logic tuning. The React architecture enabled quick modifications and real-time feedback, essential for the evolving nature of cybersecurity threat detection.

Future Directions

Considering scalability, integrating external APIs for domain reputation, deploying model training for pattern recognition, and adding comprehensive visualization are the next steps. The project illustrates React’s capacity to adapt to complex, security-critical applications even under significant initial uncertainty.

Conclusion

While documentation guides development, it’s often possible to create robust solutions through adaptive reasoning, modular design, and a clear focus on problem-relevant logic. React's component-centric approach proved invaluable in building a maintainable, scalable phishing detection interface in a documentation-sparse environment.

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🛠️ QA Tip

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