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API-Gateway-Zugriffslogs analysieren

Analysiert API-Gateway-Zugriffslogs (AWS API Gateway, Kong, Nginx), um Angriffsmuster wie BOLA, übermäßige Datenpreisgabe und Injection-Versuche zu erkennen.

Was ist API-Gateway-Zugriffslogs analysieren?

Diese KI-Fertigkeit ermöglicht die automatisierte Analyse von API-Gateway-Zugriffslogs zur Erkennung sicherheitsrelevanter Angriffsmuster. Sie unterstützt gängige API-Gateway-Plattformen wie AWS API Gateway, Kong und Nginx. Die Fertigkeit identifiziert unter anderem Broken Object Level Authorization (BOLA) durch sequentielle Ressourcen-ID-Aufzählung, Excessive Data Exposure, Rate-Limit-Umgehungen, Credential-Scanning (erkennbar an 401 Surges) sowie SQL/NoSQL-Injection-Versuche in Query-Parametern. Sie ist ideal für SOC-Analysten, die strukturierte Verfahren zur Untersuchung von Sicherheitsvorfällen benötigen, und eignet sich für den Aufbau von Detektionsregeln und Threat-Hunting-Queries. Die Umsetzung basiert auf Python 3.8+ und Pandas zur Log-Auswertung, wobei die Analyse ungewöhnliche HTTP-Methoden und Anomalien in den Access Logs berücksichtigt.

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Dokumentation

Was macht API-Gateway-Zugriffslogs analysieren?

Verwendungszweck

  • Bei der Untersuchung von Sicherheitsvorfällen, die eine Analyse von API-Gateway-Zugriffslogs erfordern
  • Beim Erstellen von Erkennungsregeln oder Threat-Hunting-Abfragen für diesen Bereich
  • Wenn SOC-Analysten strukturierte Verfahren für diese Analyseart benötigen
  • Bei der Validierung der Sicherheitsüberwachungsabdeckung für verwandte Angriffstechniken

Voraussetzungen

  • Vertrautheit mit Sicherheitsbetriebskonzepten und -tools
  • Zugriff auf eine Test- oder Laborumgebung für die sichere Ausführung
  • Python 3.8+ mit installierten erforderlichen Abhängigkeiten
  • Entsprechende Autorisierung für alle Testaktivitäten

Anleitung

Analysieren Sie API-Gateway-Zugriffslogs, um Angriffsmuster zu identifizieren, darunter Broken Object Level Authorization (BOLA), übermäßige Datenpreisgabe und Injection-Versuche.

import pandas as pd

df = pd.read_json("api_gateway_logs.json", lines=True)
# BOLA erkennen: gleicher Benutzer greift auf viele verschiedene Ressourcen-IDs zu
bola = df.groupby(["user_id", "endpoint"]).agg(
    unique_ids=("resource_id", "nunique")).reset_index()
suspicious = bola[bola["unique_ids"] > 50]

Wichtige Erkennungsmuster:

  1. BOLA/IDOR: sequentielle Ressourcen-ID-Aufzählung
  2. Rate-Limit-Umgehung durch Header-Manipulation
  3. Credential-Scanning (401 Surges von einer einzelnen Quelle)
  4. SQL/NoSQL-Injection in Query-Parametern
  5. Ungewöhnliche HTTP-Methoden (DELETE, PATCH) auf schreibgeschützten Endpunkten

Beispiele

# 401 Surges erkennen, die auf Credential-Scanning hindeuten
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

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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

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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