Hunting For Defense Evasion Via Timestomping

Hunting For Defense Evasion Via Timestomping

Overview

Cybersecurity skill for hunting for defense evasion via timestomping. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "hunting for defense evasion via timestomping"

• "Detect NTFS timestamp manipulation (MITRE T1070"

• Investigating suspected anti-forensic activity where an adversary may have altered file timestamps to blend malware into legitimate directories

• Threat hunting for defense evasion (MITRE ATT&CK T1070.006) across compromised Windows systems

• Validating timeline integrity during forensic examinations of disk images or live acquisitions

• Triaging suspicious files that appear to have creation dates older than the OS installation or inconsistent with known deployment timelines

• Detecting tools like Timestomp (Metasploit), NTimeStomp, SetMACE, or PowerShell Set-ItemProperty used to alter timestamps

• Building automated detection pipelines that flag temporal anomalies in MFT data for SOC analysts

Do not use as the sole detection method; advanced adversaries can manipulate both $STANDARD_INFORMATION and $FILE_NAME timestamps (though the latter requires raw disk access and is much harder). Combine with USN Journal, $LogFile, and ShimCache/Amcache analysis for corroboration.

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

• Raw $MFT file extracted from a Windows system (via FTK Imager, KAPE, or live extraction)
• MFTECmd (Eric Zimmerman tool) or analyzeMFT for MFT parsing
• Python 3.8+ with pandas for analysis
• Optional: mft Python library (pip install mft) for programmatic MFT parsing
• Optional: KAPE (Kroll Artifact Parser and Extractor) for automated artifact collection
• Timeline Explorer or Excel for visual analysis of parsed MFT output

Workflow

# 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. Define Detection Scope — Identify the specific techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. Collect Baseline Data — Gather historical logs and establish normal behavior patterns for .
3. Build Detection Queries — Write defense evasion via timestomping queries targeting indicators. Use platform-specific query language for optimal performance.
4. Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
5. Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

Tools

• defense evasion via timestomping — Primary tool for this skill
• SIEM Platform — Central log aggregation and query execution
• Sigma Rules — Vendor-agnostic detection rule format
• MITRE ATT&CK Navigator — Technique mapping and coverage analysis

Process

1. Reconnaissance — Gather target information, identify attack surface, enumerate services
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. Reporting — Document IOCs, write findings, provide remediation recommendations

Verification

• All 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