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Analyse von Webserver-Logs auf Eindringversuche

Parst Apache- und Nginx-Zugriffslogs, um SQL-Injection-Versuche zu erkennen.

Was ist Analyse von Webserver-Logs auf Eindringversuche?

Diese Fähigkeit ermöglicht es einem KI-Agenten, Webserver-Zugriffslogs (Apache Combined Log Format, Nginx Standardformat) systematisch auf Eindringversuche zu analysieren. Sie unterstützt die Erkennung von Angriffstechniken wie SQL-Injection, Local File Inclusion (LFI), Cross-Site Scripting (XSS), Scanner-Signaturen und Brute-Force-Versuchen. Der Agent kann mit GeoIP-Daten angereicherte Berichte erstellen und priorisierte Ergebnisse liefern. Entwickelt für SOC-Analysten, Sicherheitsingenieure und Threat-Hunting-Spezialisten, die strukturierte und automatisierte Log-Analysen benötigen. Unterstützte Plattformen umfassen Python-basierte Umgebungen (3.8+) mit den Abhängigkeiten geoip2 und user-agents. Die Fähigkeit kann über das integrierte CLI-Skript `scripts/agent.py` ausgeführt werden.

Funktioniert mit~Claude Code~Codex CLI~Cursor
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Dokumentation

Was macht Analyse von Webserver-Logs auf Eindringversuche?

Verwendungszweck

  • Bei der Untersuchung von Sicherheitsvorfällen, die eine Analyse von Webserver-Logs auf Eindringversuche 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 Konzepten und Tools der Sicherheitsbetriebsführung
  • Zugang zu einer Test- oder Laborumgebung für die sichere Ausführung
  • Python 3.8+ mit den erforderlichen Abhängigkeiten
  • Entsprechende Autorisierung für alle Testaktivitäten

Anleitung

  1. Abhängigkeiten installieren: pip install geoip2 user-agents
  2. Webserver-Zugriffslogs im Combined Log Format (Apache) oder Nginx-Standardformat sammeln.
  3. Jeden Logeintrag parsen und extrahieren: IP, Zeitstempel, Methode, URI, Statuscode, Antwortgröße, User-Agent, Referer.
  4. Erkennungsregeln anwenden:
    • SQL-Injection: UNION SELECT, OR 1=1, ' OR ', Hex-Kodierungsmuster
    • LFI/Pfadtraversierung: ../, /etc/passwd, /proc/self, php://filter
    • XSS: <script>, javascript:, onerror=, onload=
    • Scanner-Signaturen: User-Agents von nikto, sqlmap, dirbuster, gobuster, wfuzz
    • Brute-Force: >50 POST-Anfragen an Login-Endpunkte von derselben IP innerhalb von 5 Minuten
  5. Mit GeoIP-Daten anreichern und einen priorisierten Ergebnisbericht erstellen.
python scripts/agent.py --log-file /var/log/nginx/access.log --geoip-db GeoLite2-City.mmdb --output web_intrusion_report.json

Beispiele

SQLi im URI erkennen

192.168.1.100 - - [15/Jan/2024:10:30:45 +0000] "GET /products?id=1' UNION SELECT username,password FROM users-- HTTP/1.1" 200 4532

Scanner-User-Agent-Erkennung

Nikto/2.1.6, sqlmap/1.7, DirBuster-1.0-RC1, gobuster/3.1.0

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-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

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,

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