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mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
95 lines
2.5 KiB
Markdown
95 lines
2.5 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
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hollowing, injected code via VAD anomalies, hidden processes, and rootkit detection.
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Applies plugins like pslist, psscan, vadinfo, malfind, and dlllist to extract forensic
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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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- memory-forensics
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- rekall
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- process-hollowing
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- code-injection
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- vad-analysis
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- incident-response
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- security-operations
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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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mitre_attack:
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- T1078
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- T1190
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- T1059
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- T1055
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- T1005
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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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