What it does. Parse Apache and Nginx access logs to detect SQL injection attempts, Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-web-server-logs-for-intrusion, or copy the skill folder into ~/.claude/skills/analyzing-web-server-logs-for-intrusion/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/analyzing-web-server-logs-for-intrusion/SKILL.md

SKILL.md (verbatim)

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

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Web Server Log Intrusion Analysis

Combined Log Format (Apache/Nginx)

<ip> <ident> <authuser> [<date>] "<method> <uri> <proto>" <status> <size> "<referer>" "<user-agent>"

Python re Module - Log Parsing

import re
pattern = re.compile(
    r'(?P<ip>\S+) \S+ \S+ \[(?P<time>[^\]]+)\] '
    r'"(?P<method>\S+) (?P<uri>\S+) (?P<proto>[^"]*)" '
    r'(?P<status>\d+) (?P<size>\S+) "(?P<referer>[^"]*)" "(?P<ua>[^"]*)"'
)
match = pattern.match(line)
data = match.groupdict()

GeoIP2 Python Library

import geoip2.database
reader = geoip2.database.Reader("GeoLite2-City.mmdb")
response = reader.city("8.8.8.8")
response.country.name       # "United States"
response.city.name           # "Mountain View"
response.location.latitude   # 37.386
response.location.longitude  # -122.0838
reader.close()

Attack Signature Categories

Scanner User-Agent Signatures

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