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Mohammad Waseem
Mohammad Waseem

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Detecting Phishing Patterns with Zero-Budget API Development

#api #security #phishing

Detecting Phishing Patterns with Zero-Budget API Development

In the landscape of cybersecurity, detecting phishing sites and patterns is crucial but often constrained by limited resources. This blog explores how a security researcher can leverage free, open-source tools to build an effective phishing pattern detection system using API development, all without incurring costs.

The Challenge

Many organizations lack the budget for commercial threat intelligence services. However, open-source and free tools provide an alternative pathway. The key is to develop a lightweight API that can process URLs and identify potential phishing indicators based on pattern analysis.

Approach Overview

Our approach involves three core components:

  1. Data Collection: Utilize publicly available lists and free APIs.
  2. Pattern Recognition: Implement pattern matching techniques to identify common phishing traits.
  3. API Development: Build a RESTful API to serve phishing detection functionalities.

Step 1: Data Collection

Start by aggregating known malicious domains, URLs, and phishing indicators from free sources such as PhishTank, URLScan.io, or AbuseIPDB. For example, PhishTank offers downloadable datasets and API access.

import requests

# Example: Fetch recent phishing URLs from PhishTank API
response = requests.get('https://phishtank.org/api/session')
if response.status_code == 200:
    phishing_data = response.json()
    # Store for pattern analysis
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Step 2: Pattern Recognition

Analyze the dataset for common traits such as suspicious URL structures, similar subdomain patterns, or unusual query parameters. Use regex and string analysis for pattern matching.

import re

def is_suspicious_url(url):
    pattern = r"(?:\w+\.){2,}"  # e.g., multiple subdomains
    if re.search(pattern, url):
        return True
    return False
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Step 3: API Development

Employ Python with Flask framework, which is free and easy to deploy. This API provides an endpoint to check URLs against phishing patterns.

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/detect', methods=['POST'])
def detect_phishing():
    data = request.get_json()
    url = data.get('url')
    result = is_suspicious_url(url)
    return jsonify({'url': url, 'is_phishing': result})

if __name__ == '__main__':
    app.run(debug=True, port=5000)
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This lightweight API can be integrated into security dashboards, email gateways, or custom monitoring tools. It can be further enhanced by adding more pattern detection rules, integrating with free threat intelligence feeds, or deploying as a serverless function for scalability.

Conclusion

Building a phishing detection system without a budget is feasible by leveraging free data sources, pattern analysis, and open-source tools. The described API is a starting point: it provides a foundation upon which more sophisticated detection methods—such as machine learning models trained on publicly available datasets—can be built. The key is leveraging community resources and simple, effective code to improve security posture at minimal cost.

Final note

Always validate the accuracy of your detection rules and be mindful of false positives. Continuous updating of patterns and datasets is crucial for maintaining effectiveness against evolving phishing tactics.

🛠️ QA Tip

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

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