analyzing-api-gateway-access-logs

name	analyzing-api-gateway-access-logs
description	Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
domain	cybersecurity
tags	["analyzing","api","gateway","access"]
subdomain	security-operations
version	1.0
author	mahipal
license	Apache-2.0
nist_csf	["DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02"]

Analyzing Api Gateway Access Logs

Overview

Cybersecurity skill for analyzing api gateway access logs. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "analyzing api gateway access logs"

• "When investigating security incidents that require analyzing api gateway access"

• "When building detection rules or threat hunting queries for this domain"

• "When SOC analysts need structured procedures for this analysis type"

• When investigating security incidents that require analyzing api gateway access logs

• 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

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

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

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Scope the Analysis — Define what api gateway access logs artifacts or data sources to examine and the investigation timeline.
2. Preserve Evidence — Create forensic copies of relevant data. Maintain chain of custody documentation.
3. Extract Key Indicators — Parse and extract relevant api gateway access logs data points from collected artifacts.
4. Correlate Findings — Cross-reference extracted data with other sources (threat intel, logs, timelines).
5. Build Timeline — Construct a chronological sequence of events related to api gateway access logs.
6. Document Analysis — Write findings report with evidence, conclusions, and recommendations.

Tools

• Forensic Toolkit — Evidence collection and analysis
• Timeline Tools — Chronological event reconstruction
• Log Analysis Platform — Centralized log parsing and search

Process

1. Design — Define interface, identify patterns, plan implementation
2. Implement — Write code following existing conventions, add tests
3. Verify — Run tests, check integration, validate behavior

Verification

• All api gateway access logs procedures executed completely and documented
• Findings validated against multiple data sources
• False positives identified and filtered
• Results documented with evidence and timestamps
• Recommendations provided with risk-based prioritization

Anti-Rationalization

Rationalization	Reality
"We are too small to be targeted"	Automated attacks target everyone. Size does not matter.
"Security slows us down"	A breach slows you down 100x more. Build security in from the start.
"We will fix it after launch"	Vulnerabilities in production are exploited within hours. Fix before deploy.