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Mohammad Waseem
Mohammad Waseem

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Leveraging SQL to Detect Phishing Patterns in Legacy Codebases

#security #sql #legacy

Detecting Phishing Patterns Using SQL in Legacy Systems

In today’s cybersecurity landscape, identifying phishing attempts remains a top priority, especially within critical legacy systems where modernization may be ongoing or limited. As a Lead QA Engineer, I’ve faced the challenge of implementing effective detection mechanisms without overhauling existing infrastructure. This post explores how SQL—traditionally viewed as a data retrieval language—can be harnessed to identify suspicious patterns indicative of phishing attacks, even within legacy codebases.

The Challenges of Legacy Codebases

Legacy applications often lack modern security feature integrations, making them vulnerable to sophisticated phishing schemes. They typically store user data, email logs, and transaction records in relational databases, presenting an opportunity to analyze this data directly with SQL queries. However, crafting these queries requires understanding common phishing tactics and how they manifest in stored data.

Pattern Recognition—What Are We Looking For?

Phishing exploits often involve certain telltale signs:

  • Multiple login attempts from unrecognized locations or IP addresses.
  • Unusual email patterns, such as mismatched sender addresses or obfuscated URLs.
  • Frequent use of specific keywords in communications (e.g., "urgent", "verify", "account").
  • Links or call-to-action buttons pointing to suspicious domains.

Analyzing these patterns can be complex, but SQL's powerful querying capabilities enable us to sift through large datasets efficiently.

Practical SQL Techniques for Phishing Detection

1. Identifying Unusual Login Activity 

SELECT user_id, ip_address, login_time
FROM user_logins
WHERE login_time >= NOW() - INTERVAL '1 day'
GROUP BY user_id, ip_address
HAVING COUNT(*) > 10;
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This query highlights users with an unusual number of login attempts from a single IP, potentially indicating credential stuffing or brute-force attempts.

2. Detecting Suspicious Email Content 

SELECT email_id, sender_address, subject
FROM emails
WHERE subject ~* '(urgent|verify|update)'
AND sender_address NOT LIKE '%trusted-domain.com';
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Here, pattern matching helps surface emails with common phishing language from unfamiliar senders.

3. Spotting Malicious URLs 

SELECT email_id, body_text
FROM emails
WHERE body_text LIKE '%http://%' OR body_text LIKE '%https://%'
AND body_text ~* '(bit.ly|tinyurl|malicious-domain.com)';
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This detects emails containing URLs shortened or pointing to malicious domains commonly used in phishing.

4. Cross-referencing Unusual Keywords 

SELECT email_id, body_text
FROM emails
WHERE body_text ~* '(account|bank|password|login)'
AND email_id NOT IN (
  SELECT email_id FROM emails WHERE sender_address LIKE '%banking-trusted.com%'
);
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This helps identify emails with sensitive keywords originating from untrusted sources.

Automating & Integrating Detection

While manual querying is effective, automating these checks within your legacy system is crucial. Scheduling periodic SQL jobs, integrating these queries into your monitoring dashboards, or setting up alert triggers can significantly improve response times and incident handling.

Limitations & Best Practices

  • SQL-based detection should be complemented with other security measures like machine learning models or heuristic analysis.
  • Regularly update the patterns and signatures as phishing tactics evolve.
  • Ensure detailed logging to facilitate forensic investigations later.

Final Thoughts

By leveraging SQL’s capabilities for pattern recognition, QA teams can proactively identify potential phishing threats within legacy applications. Even without modern security tools, data-driven analysis remains a powerful approach to safeguard critical systems. Embracing these techniques enhances your security posture and supports ongoing efforts in cybersecurity resilience.

Remember: The key to success is understanding your data, continuously refining your detection queries, and integrating these insights into your security workflows to stay ahead of emerging threats.

🛠️ QA Tip

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

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