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 subdomain: security-operations tags:

• api-security
• access-log-analysis
• aws-api-gateway
• kong
• nginx
• bola-detection
• rate-limit-bypass
• 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

mitre_attack:

• T1190
• T1110.004
• T1078.004
• T1119

Analyzing API Gateway Access Logs

When to Use

• 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

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

Parse API gateway access logs to identify attack patterns including broken object level authorization (BOLA), excessive data exposure, and injection attempts.

import pandas as pd
df = pd.read_json("api_gateway_logs.json", lines=True)
# Detect BOLA: same user accessing many different resource IDs
bola = df.groupby(["user_id", "endpoint"]).agg(
    unique_ids=("resource_id", "nunique")).reset_index()
suspicious = bola[bola["unique_ids"] > 50]

Key detection patterns:

1. BOLA/IDOR: sequential resource ID enumeration
2. Rate limit bypass via header manipulation
3. Credential scanning (401 surges from single source)
4. SQL/NoSQL injection in query parameters
5. Unusual HTTP methods (DELETE, PATCH) on read-only endpoints

Examples

# Detect 401 surges indicating credential scanning
auth_failures = df[df["status_code"] == 401]
scanner_ips = auth_failures.groupby("source_ip").size()
scanners = scanner_ips[scanner_ips > 100]