SafeLine WAF Log Analysis in Action: 3 Key Techniques to Uncover Business Issues

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Many people who deploy SafeLine WAF focus mainly on “blocked requests” as the primary metric, overlooking the hidden value in the logs. By analyzing the protection logs, you can not only uncover potential security risks but also identify business issues that might otherwise go unnoticed. Based on my experience analyzing logs over the past six months, I’ve compiled three practical techniques that can help you extract valuable business insights directly from your WAF logs.

Understanding SafeLine Logs: Key Components

SafeLine WAF logs are typically outputted to either container logs or local log files, and each entry contains more than 10 key fields. Below is an overview of the most important fields and their potential value:

Field Name Meaning Practical Value
client_ip Client’s IP address Identify high-frequency request IPs, regional traffic distribution
path Requested URL path Analyze popular endpoints and suspicious URL access patterns
method HTTP method (GET, POST, etc.) Detect abnormal request methods (e.g., GET requests with form data)
status Response status (block, pass, log) Understand what’s being blocked vs. allowed, and why
user_agent Client browser or tool identifier Detect crawlers, suspicious clients, or unusual agents

To view the logs, you can use the following commands:

  • Container logs: docker logs -f safeline
  • Local logs: Default path is /data/logs/safeline/.

Technique 1: Identifying Ineffective Crawlers to Optimize Server Resources

Issue:

The server’s CPU usage spikes regularly, but the business traffic volume is relatively low. Upon reviewing the SafeLine logs, I noticed an unusual number of requests.

Analysis Steps:

  1. Filter logs by user agents containing “Spider” or “Bot”: 
   grep -E "Spider|Bot" /data/logs/safeline/access.log
  1. Identify high-frequency crawler IPs: 
   grep -E "Spider|Bot" access.log | awk '{print $1}' | sort | uniq -c | sort -nr | head -10
  1. Compare these IPs against legitimate crawlers like Googlebot and Baidu’s IP ranges. I discovered three IP addresses from unknown crawlers that were making over 200 requests per minute.

Solution:

  • Add these three IPs to the SafeLine WAF “IP Blacklist” under the Rule Management section, setting their block duration to permanent.
  • After applying the changes, the server’s CPU usage dropped from 70% to 30%, significantly reducing resource consumption.

Technique 2: Pinpointing Business API Bugs to Preemptively Fix Issues

Issue:

I noticed recurring 400 error responses from the /api/order/create endpoint in the logs, where requests were marked as “pass” (not blocked).

Analysis Steps:

  1. Filter logs for errors on the /api/order/create endpoint: 
   grep "/api/order/create" access.log | grep "400"
  1. Check request parameters and find that the “amount” parameter was being set to negative values in some requests.
  2. After contacting the development team, I found that the frontend had no validation for negative numbers, allowing invalid data to be submitted.

Solution:

  • The development team added validation on the frontend to prevent negative amounts.
  • I created a temporary rule in SafeLine to intercept requests to /api/order/create with “amount < 0” to prevent invalid requests from consuming backend resources.

Technique 3: Analyzing Access Patterns to Optimize User Experience

Issue:

Wanting to optimize page load times, I needed to know which pages were the most frequently accessed by users.

Analysis Steps:

  1. Identify popular page requests by analyzing paths in the logs: 
   grep "pass" access.log | awk '{print $7}' | sort | uniq -c | sort -nr | head -10
  1. I found that the /product/detail and /cart pages were responsible for 60% of the requests, but their response times were high (over 1 second, as noted in the request_time field).
  2. Upon further investigation with backend monitoring, I realized that these pages were slow due to missing database indexes.

Solution:

  • The development team added indexes to the relevant database queries.
  • As a result, page response times dropped to under 300ms, significantly improving the user experience.

Recommended Tools for Log Analysis

  1. For Light Use: You can analyze logs using basic Linux commands like grep, awk, and sort. These tools are sufficient for basic use cases and don’t require additional setup.

  2. For Medium-Scale Use: If you need more advanced log aggregation and visualization, deploy ELK Stack (Elasticsearch, Logstash, Kibana). This will allow for centralized log storage and powerful querying and visualization.

  3. For Newcomers: If you’re just getting started, SafeLine’s Log Center in the console provides filtering and statistical features, allowing you to easily filter logs by path, IP, or status without needing to deploy additional tools.

Log Retention and Backup Recommendations

  1. Enable Log Rotation: In SafeLine’s System Configuration, set log retention for 7 days to avoid disk space issues.

  2. Backup Critical Logs: Periodically back up logs for core business APIs to object storage (e.g., AWS S3 or OSS) to facilitate troubleshooting and traceability.

  3. Mask Sensitive Information: If logs contain sensitive information like phone numbers or IDs, make sure to enable data masking in the SafeLine configuration.

Conclusion: The Business Value in SafeLine Logs

SafeLine WAF’s logs are not just records of protection events—they also serve as a rich source of business intelligence. By applying simple log analysis techniques, you can uncover issues such as bot traffic, API bugs, and performance bottlenecks, turning your WAF into an indispensable tool for both security and business optimization. This “1+1=3” approach—using logs for both protection and operational improvement—can significantly enhance your system’s performance and reliability.

If you’re working with SafeLine or any WAF, I highly recommend investing time in learning how to interpret logs. It’s a valuable skill that will help you proactively address issues and improve your platform’s performance, security, and user experience.

Official Website: https://safepoint.cloud/landing/safeline
GitHub: https://github.com/chaitin/SafeLine
Discord: https://discord.gg/st92MpBkga

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