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分析 API 閘道存取日誌

解析 API 閘道存取日誌(AWS API Gateway、Kong、Nginx),以偵測攻擊模式。

分析 API 閘道存取日誌 是什麼?

此技能專為安全分析師與工程師設計,用於解析 API 閘道(如 AWS API Gateway、Kong、Nginx)的存取日誌,以識別攻擊模式。它能自動偵測諸如物件層級授權缺陷(BOLA/IDOR)、大量資料暴露、SQL/NoSQL 注入嘗試、暴力破解及速率限制繞過等威脅。技能透過 Python(pandas)對日誌進行結構化分析,並提供清晰的偵測規則與範例程式碼,支援安全監控與威脅獵捕工作流程。適用於安全營運中心(SOC)調查、事件回應以及驗證安全覆蓋範圍的場景。

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說明文件

分析 API 閘道存取日誌 是做什麼的?

何時使用

  • 調查安全事件,需分析 API 閘道存取日誌時
  • 為此領域建立偵測規則或威脅獵捕查詢時
  • SOC 分析師需要此分析類型的結構化程序時
  • 驗證相關攻擊手法之安全監控覆蓋率時

前提條件

  • 熟悉安全營運概念與工具
  • 可存取測試或實驗環境以安全執行
  • 已安裝 Python 3.8+ 及所需相依套件
  • 具備適當的測試活動授權

操作說明

解析 API 閘道存取日誌,識別攻擊模式,包括物件層級授權缺陷(BOLA)、大量資料暴露以及注入嘗試。

import pandas as pd

df = pd.read_json("api_gateway_logs.json", lines=True)
# 偵測 BOLA:同一使用者存取大量不同資源 ID
bola = df.groupby(["user_id", "endpoint"]).agg(
    unique_ids=("resource_id", "nunique")).reset_index()
suspicious = bola[bola["unique_ids"] > 50]

關鍵偵測模式:

  1. BOLA/IDOR:連續資源 ID 列舉
  2. 透過標頭操作繞過速率限制
  3. 憑證掃描(來自單一來源的大量 401 錯誤)
  4. 查詢參數中的 SQL/NoSQL 注入
  5. 對唯讀端點使用異常 HTTP 方法(DELETE、PATCH)

範例

# 偵測 401 突增,表示憑證掃描
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

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

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

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

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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