DEV Community

Mohammad Waseem
Mohammad Waseem

Posted on

Detecting Phishing Patterns in SQL: A DevOps Approach Without Documentation

#devops #security #sql

Introduction

In the fight against cybersecurity threats, phishing remains a top concern. Detecting malicious patterns within email links or URLs is crucial, yet often overlooked due to limited or absent documentation, especially in fast-paced DevOps environments. Leveraging SQL for pattern detection offers a scalable and efficient solution that can be integrated seamlessly into existing data pipelines.

The Challenge

Without proper documentation, understanding how to identify potential phishing URLs or email patterns can be challenging. The goal is to create a reliable SQL-based detection mechanism that recognizes common traits of phishing URLs—such as suspicious domain names, unusual subdomain structures, or known malicious keywords—without relying on external libraries or extensive comments.

Approach

Our strategy involves analyzing patterns directly within the database, focusing on features like domain entropy, the use of symbols, length anomalies, and suspicious keywords. This method demands a clear understanding of typical phishing URL characteristics and translating that understanding into efficient SQL queries.

Key Detection Patterns

  1. Suspicious Keywords: Common words used maliciously in URLs, e.g., "login", "verify", "secure".
  2. Unusual Lengths: Extremely long or short URLs.
  3. Subdomain Analysis: Excessive or suspicious subdomain usage.
  4. Character Patterns: Use of special characters like "@" or multiple hyphens.
  5. Domain Reputation Indicators: Accessing known malicious domains (if a threat intelligence table exists).

Below are SQL snippets illustrating how to detect each pattern.

SQL Implementation

1. Detect Suspicious Keywords 

SELECT url, CASE WHEN url LIKE '%login%' OR url LIKE '%verify%' OR url LIKE '%secure%' THEN 'Suspicious Keyword' END AS pattern
FROM email_links
WHERE url LIKE '%login%' OR url LIKE '%verify%' OR url LIKE '%secure%';
Enter fullscreen mode Exit fullscreen mode

This query flags URLs containing common malicious keywords.

2. Detect Abnormal Lengths 

SELECT url, LENGTH(url) AS url_length
FROM email_links
WHERE LENGTH(url) > 100 OR LENGTH(url) < 10;
Enter fullscreen mode Exit fullscreen mode

Identifies URLs that could be either too long or too short, possibly indicative of obfuscation.

3. Analyze Subdomain Structures 

SELECT url, SUBSTRING_INDEX(SUBSTRING_INDEX(url, '://', -1), '/', 1) AS domain,
       (LENGTH(SUBSTRING_INDEX(SUBSTRING_INDEX(url, '://', -1), '/', 1)) - LENGTH(REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(url, '://', -1), '/', 1), '.', '')) ) AS dot_count
FROM email_links
WHERE dot_count > 3;
Enter fullscreen mode Exit fullscreen mode

This detects URLs with excessive subdomains, often used in phishing.

4. Detect Special Character Usage 

SELECT url
FROM email_links
WHERE url LIKE '%@%' OR url LIKE '%--%' OR url LIKE '%//%';
Enter fullscreen mode Exit fullscreen mode

Flags URLs with characters that could be used to manipulate URLs or conceal malicious intent.

5. Check Against Malicious Domains

If a "malicious_domains" table exists:

SELECT el.url, md.domain
FROM email_links el
JOIN malicious_domains md ON el.domain = md.domain;
Enter fullscreen mode Exit fullscreen mode

This joins your email links with known bad domains.

Integrating the Detection System

While this implementation is straightforward, integrating it into your DevOps pipeline involves scheduling these queries to run periodically and flagging suspicious URLs for further manual review or automated blocking.

Conclusion

Detecting phishing patterns purely through SQL requires understanding of common attack vectors and translating these into leverage points within your database. Despite the challenge posed by minimal documentation, a systematic approach applying pattern recognition queries like those shown can significantly improve your detection capabilities. However, always complement SQL-based detection with other layers of security, such as machine learning models or external threat intelligence.

Final Thoughts

This approach emphasizes the importance of understanding your data and attack patterns, especially when documentation is lacking. Efficient SQL detection provides agility and speed—critical in responding to rapid threat evolution—ensuring you maintain a robust security posture.

Note: Always validate these queries against a subset of your data to calibrate thresholds and improve accuracy over time.

🛠️ QA Tip

Pro Tip: Use TempoMail USA for generating disposable test accounts.

Top comments (0)