Analyzing Prefetch Files For Execution History

---
name: analyzing-prefetch-files-for-execution-history
description: Use when parse Windows Prefetch files to determine program execution
  history including run counts, timestamps, and referenced files for forensic investigation.
  Use when working with analyzing prefetch files for execution history.
domain: cybersecurity
tags:
- forensics
- prefetch
- windows-artifacts
- execution-history
- timeline-analysis
- evidence-collection
subdomain: digital-forensics
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- RS.AN-01
- RS.AN-03
- DE.AE-02
- RS.MA-01
category: cybersecurity
---

# Analyzing Prefetch Files For Execution History

## Overview

Cybersecurity skill for analyzing prefetch files for execution history. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "analyzing prefetch files for execution history"
- "Parse Windows Prefetch files to determine program execution history including ru"

- When determining which programs were executed on a Windows system and when
- During malware investigations to confirm execution of suspicious binaries
- For establishing a timeline of application usage during an incident
- When correlating program execution with other forensic artifacts
- To identify anti-forensic tools or unauthorized software that was run


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## Prerequisites
- Access to Windows Prefetch directory (C:\Windows\Prefetch\) from forensic image
- PECmd (Eric Zimmerman), WinPrefetchView, or python-prefetch parser
- Understanding of Prefetch file format (versions 17, 23, 26, 30)
- Windows system with Prefetch enabled (default on client OS, disabled on servers)
- Knowledge of Prefetch naming conventions (APPNAME-HASH.pf)

## Workflow

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Scope the Analysis** — Define what prefetch files artifacts or data sources to examine and the investigation timeline.
2. **Preserve Evidence** — Create forensic copies of relevant data. Maintain chain of custody documentation.
3. **Extract Key Indicators** — Use execution history to parse and extract relevant prefetch files data points from collected artifacts.
4. **Correlate Findings** — Cross-reference extracted data with other sources (threat intel, logs, timelines).
5. **Build Timeline** — Construct a chronological sequence of events related to prefetch files.
6. **Document Analysis** — Write findings report with evidence, conclusions, and recommendations.

## Tools

- **execution history** — Primary tool for this skill
- **Forensic Toolkit** — Evidence collection and analysis
- **Timeline Tools** — Chronological event reconstruction
- **Log Analysis Platform** — Centralized log parsing and search


## Process

1. **Scope** — Define research questions, identify data sources, set time boundaries
1. **Gather** — Collect data from primary sources, APIs, and public records
1. **Synthesize** — Analyze findings, identify patterns, produce actionable report

## Verification

- [ ] All prefetch files procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |