analyzing-docker-container-forensics

name	analyzing-docker-container-forensics
description	Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence. Use when working with analyzing docker container forensics.
domain	cybersecurity
tags	["forensics","docker","container-forensics","container-security","image-analysis","runtime-investigation"]
subdomain	digital-forensics
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["RS.AN-01","RS.AN-03","DE.AE-02","RS.MA-01"]

Analyzing Docker Container Forensics

Overview

Cybersecurity skill for analyzing docker container forensics. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "analyzing docker container forensics"

• "Investigate compromised Docker containers by analyzing images, layers, volumes, "

• When investigating a compromised Docker container or container host

• For analyzing malicious Docker images pulled from registries

• During incident response involving containerized application breaches

• When examining container escape attempts or privilege escalation

• For auditing container configurations and identifying misconfigurations

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• Docker CLI access on the forensic workstation
• Access to the Docker host file system (forensic image or live)
• Understanding of Docker layered file system (overlay2, aufs)
• dive, docker-explorer, or container-diff for image analysis
• Knowledge of Docker daemon configuration and socket security
• Trivy or Grype for vulnerability scanning of container images

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}