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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)
85 lines
2.4 KiB
Markdown
85 lines
2.4 KiB
Markdown
---
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name: extracting-memory-artifacts-with-rekall
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description: 'Uses Rekall memory forensics framework to analyze memory dumps for process hollowing, injected code via VAD
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anomalies, hidden processes, and rootkit detection. Applies plugins like pslist, psscan, vadinfo, malfind, and dlllist to
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extract forensic artifacts from Windows memory images. Use during incident response memory analysis.
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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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- extracting
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- memory
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- artifacts
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- with
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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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# Extracting Memory Artifacts with Rekall
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## When to Use
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- When performing authorized security testing that involves extracting memory artifacts with rekall
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- When analyzing malware samples or attack artifacts in a controlled environment
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- When conducting red team exercises or penetration testing engagements
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- When building detection capabilities based on offensive technique understanding
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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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Use Rekall to analyze memory dumps for signs of compromise including process
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injection, hidden processes, and suspicious network connections.
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```python
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from rekall import session
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from rekall import plugins
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# Create a Rekall session with a memory image
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s = session.Session(
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filename="/path/to/memory.raw",
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autodetect=["rsds"],
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profile_path=["https://github.com/google/rekall-profiles/raw/master"]
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)
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# List processes
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for proc in s.plugins.pslist():
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print(proc)
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# Detect injected code
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for result in s.plugins.malfind():
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print(result)
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```
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Key analysis steps:
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1. Load memory image and auto-detect profile
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2. Run pslist and psscan to find hidden processes
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3. Use malfind to detect injected/hollowed code in process VADs
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4. Examine network connections with netscan
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5. Extract suspicious DLLs and drivers with dlllist/modules
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## Examples
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```python
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from rekall import session
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s = session.Session(filename="memory.raw")
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# Compare pslist vs psscan for hidden processes
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pslist_pids = set(p.pid for p in s.plugins.pslist())
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psscan_pids = set(p.pid for p in s.plugins.psscan())
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hidden = psscan_pids - pslist_pids
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print(f"Hidden PIDs: {hidden}")
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```
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