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API Gateway アクセスログ分析

AWS API Gateway、Kong、Nginx などの API Gateway アクセスログを解析し、攻撃パターンを検出します。

API Gateway アクセスログ分析 とは?

このスキルは、API Gateway(AWS API Gateway、Kong、Nginx など)のアクセスログを解析し、セキュリティインシデントの調査や検知ルールの構築を支援します。具体的には、BOLA(Broken Object Level Authorization)/IDOR、過剰なデータ露出、インジェクション攻撃、レート制限バイパス、クレデンシャルスキャンなどの攻撃パターンを検出するための手順を提供します。Python と pandas を用いた実践的なコード例が含まれており、SOC アナリストやセキュリティエンジニアがログデータから不審な振る舞いを効率的に特定できるよう設計されています。ユーザーID ごとのユニークなリソース ID 数の集計や、ステータスコード 401 の急増を検出するクエリなど、具体的な分析手法を習得できます。

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-api-gateway-access-logs

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ドキュメント

API Gateway アクセスログ分析 は何をしますか?

使用タイミング

  • セキュリティインシデント調査で API Gateway アクセスログの分析が必要な場合
  • このドメインに関する検知ルールや脅威ハンティングクエリを作成する場合
  • SOC アナリストがこの分析タイプの構造化された手順を必要とする場合
  • 関連する攻撃手法に対するセキュリティ監視のカバレッジを検証する場合

前提条件

  • セキュリティ運用の概念とツールに関する知識
  • 安全な実行のためのテスト環境またはラボ環境へのアクセス
  • Python 3.8 以上と必要な依存関係のインストール済み
  • テスト活動に対する適切な認可

手順

API Gateway アクセスログを解析し、BOLA(Broken Object Level Authorization / 過剰なデータ露出)やインジェクション攻撃を含む攻撃パターンを特定します。

import pandas as pd

df = pd.read_json("api_gateway_logs.json", lines=True)
# BOLA の検出:同一ユーザーが多数の異なるリソース ID にアクセス
bola = df.groupby(["user_id", "endpoint"]).agg(
    unique_ids=("resource_id", "nunique")).reset_index()
suspicious = bola[bola["unique_ids"] > 50]

主要な検出パターン:

  1. BOLA/IDOR:シーケンシャルなリソース ID の列挙
  2. ヘッダー操作によるレート制限のバイパス
  3. クレデンシャルスキャン(単一ソースからの 401 急増)
  4. クエリパラメータへの SQL/NoSQL インジェクション
  5. 読み取り専用エンドポイントへの異常な HTTP メソッド(DELETE、PATCH)

例

# クレデンシャルスキャンを示す 401 急増を検出
auth_failures = df[df["status_code"] == 401]
scanner_ips = auth_failures.groupby("source_ip").size()
scanners = scanner_ips[scanner_ips > 100]

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-apt-group-with-mitre-navigator

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analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

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analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

analyzing-docker-container-forensics

Investigate compromised Docker containers by analyzing images, layers,

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