mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-07-24 05:30:58 +03:00
mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
83 lines
2.4 KiB
Markdown
83 lines
2.4 KiB
Markdown
---
|
|
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:
|
|
- analyzing
|
|
- memory
|
|
- forensics
|
|
- with
|
|
version: '1.0'
|
|
author: mahipal
|
|
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
|
|
|
|
|
|
## 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.
|
|
|
|
```bash
|
|
# 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
|
|
```
|
|
|
|
```python
|
|
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
|
|
|
|
```bash
|
|
# 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
|
|
```
|