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analyzing-web-server-logs-for-intrusion

analyzing-web-server-logs-for-intrusion

Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: analyzing-web-server-logs-for-intrusion description: Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers. domain: cybersecurity subdomain: security-operations tags:

  • web-log-analysis
  • apache-logs
  • nginx-logs
  • sql-injection-detection
  • lfi-detection
  • directory-traversal
  • intrusion-detection version: '1.0' author: mahipal license: Apache-2.0 nist_csf:
  • DE.CM-01
  • RS.MA-01
  • GV.OV-01
  • DE.AE-02 mitre_attack:
  • T1190
  • T1059.007
  • T1110
  • T1595.002
  • T1505.003

Analyzing Web Server Logs for Intrusion

When to Use

  • When investigating security incidents that require analyzing web server logs for intrusion
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Familiarity with security operations concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

  1. Install dependencies: pip install geoip2 user-agents
  2. Collect web server access logs in Combined Log Format (Apache) or Nginx default format.
  3. Parse each log entry extracting: IP, timestamp, method, URI, status code, response size, user-agent, referer.
  4. Apply detection rules:
    • SQL injection: UNION SELECT, OR 1=1, ' OR ', hex encoding patterns
    • LFI/Path traversal: ../, /etc/passwd, /proc/self, php://filter
    • XSS: <script>, javascript:, onerror=, onload=
    • Scanner signatures: nikto, sqlmap, dirbuster, gobuster, wfuzz user-agents
    • Brute force: >50 POST requests to login endpoints from same IP in 5 minutes
  5. Enrich with GeoIP data and generate a prioritized findings report.
python scripts/agent.py --log-file /var/log/nginx/access.log --geoip-db GeoLite2-City.mmdb --output web_intrusion_report.json

Examples

Detect SQLi in URI

192.168.1.100 - - [15/Jan/2024:10:30:45 +0000] "GET /products?id=1' UNION SELECT username,password FROM users-- HTTP/1.1" 200 4532

Scanner User-Agent Detection

Nikto/2.1.6, sqlmap/1.7, DirBuster-1.0-RC1, gobuster/3.1.0
Gespeicherte Skills