mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
69 lines
2.3 KiB
Markdown
69 lines
2.3 KiB
Markdown
---
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name: analyzing-web-server-logs-for-intrusion
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description: Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal,
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web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP
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enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- analyzing
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- web
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- server
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- logs
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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---
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# Analyzing Web Server Logs for Intrusion
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## When to Use
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- When investigating security incidents that require analyzing web server logs for intrusion
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with security operations concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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1. Install dependencies: `pip install geoip2 user-agents`
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2. Collect web server access logs in Combined Log Format (Apache) or Nginx default format.
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3. Parse each log entry extracting: IP, timestamp, method, URI, status code, response size, user-agent, referer.
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4. Apply detection rules:
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- SQL injection: `UNION SELECT`, `OR 1=1`, `' OR '`, hex encoding patterns
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- LFI/Path traversal: `../`, `/etc/passwd`, `/proc/self`, `php://filter`
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- XSS: `<script>`, `javascript:`, `onerror=`, `onload=`
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- Scanner signatures: nikto, sqlmap, dirbuster, gobuster, wfuzz user-agents
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- Brute force: >50 POST requests to login endpoints from same IP in 5 minutes
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5. Enrich with GeoIP data and generate a prioritized findings report.
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```bash
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python scripts/agent.py --log-file /var/log/nginx/access.log --geoip-db GeoLite2-City.mmdb --output web_intrusion_report.json
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```
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## Examples
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### Detect SQLi in URI
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```
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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
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```
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### Scanner User-Agent Detection
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```
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Nikto/2.1.6, sqlmap/1.7, DirBuster-1.0-RC1, gobuster/3.1.0
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```
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