Nginx, a popular web server, generates numerous log entries daily, but a significant portion of these entries are invalid request records, such as redundant logs generated by crawler scans, malicious detection, and configuration errors. This invalid data can account for 30% or more of the total log volume. Intelligent clarification can help save storage costs, improve security monitoring efficiency, and identify the invalid content that is truly consuming excessive storage.
Invalid log data exhibits distinct patterns. Malicious scans often manifest as regular path probing. Attackers use automated tools to batch-attempt common vulnerable paths, such as /admin and /phpmyadmin, which are administrative backends. These requests often cluster within a short period of time, returning a large number of 404 status codes and forming a distinctive log pattern. While most automated tools can be identified by analyzing the UserAgent string, advanced attackers can forge legitimate UA information, requiring more in-depth behavioral analysis.
While search engine crawlers are considered normal traffic, excessive crawling can put strain on servers. Mainstream crawlers like Googlebot and Baiduspider have official verification methods, which can be verified through reverse DNS queries. Unverified crawlers may be data harvesting for competitors or even attack detection disguised as crawlers.
Invalid requests generated by configuration errors are also a concern. Incorrect links in front-end pages can cause user browsers to repeatedly request non-existent resources. While harmless, these requests can pollute log data. By analyzing the referrer field, you can pinpoint the specific problematic page and address invalid request issues at the source.
Implementing real-time filtering at the Nginx level is the most effective primary mitigation solution. By defining invalid UserAgent patterns in the map module, known malicious crawlers can be rejected before logging:
nginx
map $http_user_agent $invalid_agent {
default 0;
"~scanner|spider|bot" 1;
"~nmap|sqlmap" 1;
}This solution has the advantage of minimal resource consumption, but the pattern rules must be continuously updated to address new threats.
More refined filtering can be achieved by combining Nginx's if conditional with the error_log level. For probe requests to a specific path, you can reduce their log level without completely discarding the records:
nginx
location ~ ^/(admin|phpmyadmin) {
if ($http_user_agent ~ "(bot|scanner)") {
access_log off;
return 444;
}
}This solution strikes a good balance between security and log integrity, avoiding wasted storage space while preserving necessary audit trails.
The introduction of the Lua module opens up endless possibilities for real-time filtering. By embedding custom scripts, you can implement complex judgment logic based on request frequency, geographic origin, and behavior sequences. Enhanced Nginx distributions such as OpenResty excel in this area and are suitable for scenarios with high security requirements.
Log Post-Processing and Archiving Optimization
For already generated log files, post-processing based on Logrotate provides a flexible cleaning solution. By configuring a postrotate script, you can use text processing tools such as SED to remove invalid entries:
!/bin/
awk '$9 != 404 && $9 != 400' /var/log/nginx/access.log > /tmp/clean.log
mv /tmp/clean.log /var/log/nginx/access.logThis method is suitable for batch cleaning of historical logs, but it is important to ensure service continuity during the process.
Log analysis tools such as GoAccess have built-in data filtering capabilities that can eliminate invalid data during the analysis phase. The advantage of this solution is that the original logs are preserved intact, but the analysis perspective is optimized. This non-destructive processing is a more suitable option for environments with strict compliance requirements.
Machine learning algorithms bring intelligent possibilities to log cleaning. By training models to identify normal user behavior patterns, the system can automatically flag abnormal requests. While this method requires a higher initial investment, it can significantly reduce maintenance costs in the long term and improve the accuracy of threat detection.
Cleansed log data should be deeply integrated with security monitoring systems. Using SIEM platforms such as the ELK Stack or Splunk, you can establish a real-time security incident detection pipeline. Suspicious requests flagged during the scrubbing process should trigger corresponding alert rules, forming a complete defense loop.
Behavioral analysis techniques can uncover more subtle security threats. A single request may appear harmless, but analyzing it over time series can reveal attack patterns, such as scanning patterns and brute-force attacks. This analysis requires high-quality, cleansed log data, further highlighting the importance of log scrubbing.
Effective log scrubbing translates directly into significant cost savings. In cloud computing environments, log storage costs can consume a significant portion of infrastructure budgets. By removing invalid data, storage requirements can be reduced by 30%-50%, while also improving query and analysis performance.
Query performance is also significantly improved. Cleansed log files are smaller and indexes are more compact, significantly reducing response times for critical queries. In scenarios requiring real-time monitoring, this performance improvement can represent a critical window for threat response.
Establishing an automated scrubbing process is key to scalable operations. Integrating log processing scripts into CI/CD pipelines ensures continuous updates and consistent execution of scrubbing policies. This automation not only reduces labor costs but also improves the reliability of the processing process.
Log cleaning must take compliance requirements into account. Regulations such as GDPR and SMES 2.0 clearly define the retention periods and content of log data. During the cleaning process, it is important to ensure compliance with relevant regulations, consulting legal experts as necessary. Data classification policies should clearly define which log information should be retained and which can be safely deleted. For example, user personal identification information may require special handling, while general error request records may only require aggregated statistical information. Audit trails are another important consideration. All log cleaning operations should be logged in detail, forming a complete processing chain. This metadata can be crucial in security incident investigations, helping to reconstruct the timeline of events.
