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analyzing-api-gateway-access-logs

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.

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Aktualisiert1. Juni 2026 um 10:13

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mukul975

mukul975/Anthropic-Cybersecurity-Skills

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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:

BOLA/IDOR: sequential resource ID enumeration
Rate limit bypass via header manipulation
Credential scanning (401 surges from single source)
SQL/NoSQL injection in query parameters
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]

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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:

BOLA/IDOR: sequential resource ID enumeration

Rate limit bypass via header manipulation

Credential scanning (401 surges from single source)

SQL/NoSQL injection in query parameters

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]