analyzing-memory-forensics-with-lime-and-volatility

name	analyzing-memory-forensics-with-lime-and-volatility
description	Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.
domain	cybersecurity
tags	["memory-forensics","linux-forensics","lime","volatility","incident-response","kernel-modules"]
subdomain	security-operations
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02"]

Analyzing Memory Forensics With Lime And Volatility

Overview

Cybersecurity skill for analyzing memory forensics with lime and volatility. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "analyzing memory forensics with lime and volatility"

• "When investigating security incidents that require analyzing memory forensics wi"

• "When building detection rules or threat hunting queries for this domain"

• "When SOC analysts need structured procedures for this analysis type"

• When investigating security incidents that require analyzing memory forensics with lime and volatility

• When building detection rules or threat hunting queries for this domain

• When SOC analysts need structured procedures for this analysis type

• When validating security monitoring coverage for related attack techniques

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

• Familiarity with security operations concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

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()}