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mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
72 lines
2.1 KiB
Markdown
72 lines
2.1 KiB
Markdown
---
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name: analyzing-api-gateway-access-logs
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description: 'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass,
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credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection.
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Use when investigating API abuse or building API-specific threat detection rules.
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'
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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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- api
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- gateway
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- access
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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 API Gateway Access Logs
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## When to Use
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- When investigating security incidents that require analyzing api gateway access logs
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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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Parse API gateway access logs to identify attack patterns including broken object
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level authorization (BOLA), excessive data exposure, and injection attempts.
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```python
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import pandas as pd
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df = pd.read_json("api_gateway_logs.json", lines=True)
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# Detect BOLA: same user accessing many different resource IDs
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bola = df.groupby(["user_id", "endpoint"]).agg(
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unique_ids=("resource_id", "nunique")).reset_index()
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suspicious = bola[bola["unique_ids"] > 50]
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```
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Key detection patterns:
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1. BOLA/IDOR: sequential resource ID enumeration
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2. Rate limit bypass via header manipulation
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3. Credential scanning (401 surges from single source)
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4. SQL/NoSQL injection in query parameters
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5. Unusual HTTP methods (DELETE, PATCH) on read-only endpoints
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## Examples
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```python
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# Detect 401 surges indicating credential scanning
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auth_failures = df[df["status_code"] == 401]
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scanner_ips = auth_failures.groupby("source_ip").size()
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scanners = scanner_ips[scanner_ips > 100]
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```
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