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
subdomain	security-operations
tags	["memory-forensics","linux-forensics","lime","volatility","incident-response","kernel-modules"]
version	1.0
author	mahipal
license	Apache-2.0
nist_csf	["DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02"]
mitre_attack	["T1055","T1003.001","T1620","T1564.001"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

Analyzing Memory Forensics with LiME and Volatility

When to Use

• 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

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

Instructions

Acquire Linux memory using LiME kernel module, then analyze with Volatility 3 to extract forensic artifacts from the memory image.

# LiME acquisition
insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime"

# Volatility 3 analysis
vol3 -f /evidence/memory.lime linux.pslist
vol3 -f /evidence/memory.lime linux.bash
vol3 -f /evidence/memory.lime linux.sockstat

import volatility3
from volatility3.framework import contexts, automagic
from volatility3.plugins.linux import pslist, bash, sockstat

# Programmatic Volatility 3 usage
context = contexts.Context()
automagics = automagic.available(context)

Key analysis steps:

1. Acquire memory with LiME (format=lime or format=raw)
2. List processes with linux.pslist, compare with linux.psscan
3. Extract bash command history with linux.bash
4. List network connections with linux.sockstat
5. Check loaded kernel modules with linux.lsmod for rootkits

Examples

# Full forensic workflow
vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]"
vol3 -f memory.lime linux.bash
vol3 -f memory.lime linux.malfind
vol3 -f memory.lime linux.lsmod