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claude[bot]andClaude Code
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Three SKILL.md files had tags that were simply words split from the skill name (e.g., "analyzing", "block", "with", "logs") rather than meaningful discovery keywords. Replace with domain-specific terms that agents and search tools can actually use for routing. - analyzing-powershell-script-block-logging: [powershell, script-block-logging, event-id-4104, obfuscation-detection, windows-forensics, endpoint-security] - analyzing-azure-activity-logs-for-threats: [azure, cloud-security, azure-monitor, kql, threat-hunting, activity-logs] - analyzing-memory-forensics-with-lime-and-volatility: [memory-forensics, linux-forensics, lime, volatility, incident-response, kernel-modules] Co-Authored-By: Claude Code <noreply@anthropic.com>
85 lines
2.4 KiB
Markdown
85 lines
2.4 KiB
Markdown
---
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name: analyzing-memory-forensics-with-lime-and-volatility
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description: 'Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility
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3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux
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memory images. Use when performing incident response on compromised Linux systems.
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'
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- memory-forensics
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- linux-forensics
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- lime
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- volatility
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- incident-response
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- kernel-modules
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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---
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# Analyzing Memory Forensics with LiME and Volatility
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## When to Use
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- When investigating security incidents that require analyzing memory forensics with lime and volatility
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with security operations concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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Acquire Linux memory using LiME kernel module, then analyze with Volatility 3
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to extract forensic artifacts from the memory image.
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```bash
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# LiME acquisition
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insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime"
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# Volatility 3 analysis
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vol3 -f /evidence/memory.lime linux.pslist
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vol3 -f /evidence/memory.lime linux.bash
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vol3 -f /evidence/memory.lime linux.sockstat
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```
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```python
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import volatility3
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from volatility3.framework import contexts, automagic
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from volatility3.plugins.linux import pslist, bash, sockstat
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# Programmatic Volatility 3 usage
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context = contexts.Context()
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automagics = automagic.available(context)
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```
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Key analysis steps:
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1. Acquire memory with LiME (format=lime or format=raw)
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2. List processes with linux.pslist, compare with linux.psscan
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3. Extract bash command history with linux.bash
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4. List network connections with linux.sockstat
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5. Check loaded kernel modules with linux.lsmod for rootkits
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## Examples
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```bash
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# Full forensic workflow
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vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]"
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vol3 -f memory.lime linux.bash
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vol3 -f memory.lime linux.malfind
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vol3 -f memory.lime linux.lsmod
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```
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