Detect NTFS timestamp manipulation (MITRE T1070.006) by comparing $STANDARD_INFORMATION vs $FILE_NAME timestamps in the MFT. Uses analyzeMFT and Python to identify files with anomalous temporal patterns indicating anti-forensic timestomping activity.
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name
hunting-for-defense-evasion-via-timestomping
description
Detect NTFS timestamp manipulation (MITRE T1070.006) by comparing $STANDARD_INFORMATION vs $FILE_NAME timestamps in the MFT. Uses analyzeMFT and Python to identify files with anomalous temporal patterns indicating anti-forensic timestomping activity.
Vendored verbatim from an external Apache-2.0 security-skill library, pinned by commit. Exceeds the internal 300-line skill guideline (agent-code-constraints.md) -- kept as-is because this is vendored reference material (forensics/threat-intel procedure), not Yana AI-authored content, and trimming would damage technical accuracy.
Hunting for Defense Evasion via Timestomping
Detect timestamp manipulation by analyzing NTFS MFT entries for
discrepancies between $STANDARD_INFORMATION and $FILE_NAME attributes.
When to Use
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.
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
Optional: KAPE (Kroll Artifact Parser and Extractor) for automated artifact collection
Timeline Explorer or Excel for visual analysis of parsed MFT output
Workflow
Step 1: Extract the $MFT from a Live System or Disk Image
# Method 1: Using KAPE to collect MFT and related artifacts
.\kape.exe --tsource C: --tdest D:\Evidence\MFT_Collection --target !SANS_Triage
# Method 2: Using FTK Imager CLI to extract $MFT
ftkimager.exe \\.\C: D:\Evidence\mft_raw.bin --e01 --include $MFT
# Method 3: Raw copy using RawCopy (handles locked NTFS system files)
RawCopy.exe /FileNamePath:C:0 /OutputPath:D:\Evidence\ /OutputName:$MFT
# Method 4: On a mounted forensic image in Linuxsudo mount -o ro,norecovery /dev/sdb1 /mnt/evidence
sudo icat -o 2048 /dev/sdb 0 > /mnt/output/$MFT# Method 5: Using sleuthkit to extract MFT from disk image
icat -o 2048 evidence.E01 0 > extracted_MFT
Step 2: Parse the MFT with MFTECmd
Use Eric Zimmerman's MFTECmd to produce a CSV with both $STANDARD_INFORMATION and $FILE_NAME timestamps:
Step 3: Detect Timestomping via SI vs FN Comparison
The core detection: $STANDARD_INFORMATION timestamps are easily modified by user-mode tools, but $FILE_NAME timestamps are updated only by the NTFS driver (kernel-mode). When SI timestamps are OLDER than FN timestamps, timestomping is likely:
import pandas as pd
from datetime import datetime, timedelta
defload_mft_data(csv_path):
"""Load MFTECmd parsed CSV output."""
df = pd.read_csv(csv_path, low_memory=False)
# Parse timestamp columns
timestamp_cols = [
"Created0x10", "LastModified0x10", "LastAccess0x10", "LastRecordChange0x10",
"Created0x30", "LastModified0x30", "LastAccess0x30", "LastRecordChange0x30"
]
for col in timestamp_cols:
if col in df.columns:
df[col] = pd.to_datetime(df[col], errors="coerce")
return df
defdetect_timestomping(df):
"""Detect timestamp manipulation by comparing SI and FN attributes.
Key indicators:
1. SI Created < FN Created (SI timestamp pushed back in time)
2. SI timestamps have nanoseconds = 0000000 (tool artifact)
3. SI Created < FN Entry Modified (impossible under normal NTFS behavior)
4. Large gap between SI and FN timestamps
"""
results = []
for idx, row in df.iterrows():
si_created = row.get("Created0x10")
fn_created = row.get("Created0x30")
si_modified = row.get("LastModified0x10")
fn_modified = row.get("LastModified0x30")
si_entry = row.get("LastRecordChange0x10")
fn_entry = row.get("LastRecordChange0x30")
if pd.isna(si_created) or pd.isna(fn_created):
continue
filepath = row.get("FileName", "unknown")
parent_path = row.get("ParentPath", "")
full_path = f"{parent_path}\\{filepath}"if parent_path else filepath
indicators = []
# Detection 1: SI Created is BEFORE FN Created# Under normal NTFS operations, SI Created >= FN Createdif si_created < fn_created:
delta = fn_created - si_created
indicators.append({
"check": "SI_Created < FN_Created",
"si_value": str(si_created),
"fn_value": str(fn_created),
"delta": str(delta),
"confidence": "high"
})
# Detection 2: SI Modified is BEFORE FN Created# A file cannot be modified before it was createdif pd.notna(si_modified) and si_modified < fn_created:
indicators.append({
"check": "SI_Modified < FN_Created",
"si_value": str(si_modified),
"fn_value": str(fn_created),
"confidence": "high"
})
# Detection 3: Nanosecond precision check# Many timestomping tools set timestamps with zero nanosecondsif pd.notna(si_created):
si_created_str = str(si_created)
if".000000"in si_created_str or si_created_str.endswith("00:00:00"):
# Check if FN has normal nanosecond precision
fn_str = str(fn_created)
if".000000"notin fn_str:
indicators.append({
"check": "SI_nanoseconds_zeroed",
"si_value": si_created_str,
"fn_value": fn_str,
"confidence": "medium"
})
# Detection 4: Large time gap between SI and FN# Normal gap is seconds to minutes, not yearsifabs((si_created - fn_created).days) > 365:
indicators.append({
"check": "SI_FN_gap_exceeds_1_year",
"si_value": str(si_created),
"fn_value": str(fn_created),
"delta_days": abs((si_created - fn_created).days),
"confidence": "high"
})
# Detection 5: SI Entry Modified much later than SI Created# Indicates the SI attribute was rewrittenif pd.notna(si_entry) and pd.notna(si_created):
entry_delta = si_entry - si_created
if entry_delta.days > 365 * 5: # Entry modified years after creation
indicators.append({
"check": "SI_entry_modified_years_after_creation",
"si_created": str(si_created),
"si_entry_modified": str(si_entry),
"confidence": "medium"
})
if indicators:
results.append({
"file_path": full_path,
"entry_number": row.get("EntryNumber", ""),
"in_use": row.get("InUse", True),
"si_created": str(si_created),
"fn_created": str(fn_created),
"indicators": indicators,
"highest_confidence": max(i["confidence"] for i in indicators),
})
return results
# Run detection
df = load_mft_data("D:\\Evidence\\Parsed\\mft_parsed.csv")
stomped_files = detect_timestomping(df)
print(f"\nTimestomping Detection Results")
print(f"{'='*60}")
print(f"Total MFT entries analyzed: {len(df)}")
print(f"Suspicious entries found: {len(stomped_files)}")
print()
for entry insorted(stomped_files, key=lambda x: x["highest_confidence"], reverse=True):
print(f"[{entry['highest_confidence'].upper()}] {entry['file_path']}")
print(f" SI Created: {entry['si_created']}")
print(f" FN Created: {entry['fn_created']}")
for ind in entry["indicators"]:
print(f" Check: {ind['check']} (confidence: {ind['confidence']})")
print()
Step 4: Corroborate with USN Journal Analysis
The USN Journal records metadata change events that persist even after timestomping:
defcorrelate_with_usn_journal(stomped_files, usn_csv_path):
"""Cross-reference timestomped files with USN Journal entries.
The USN Journal records a BASIC_INFO_CHANGE reason when timestamps
are modified, providing corroborating evidence of timestomping.
"""
usn_df = pd.read_csv(usn_csv_path, low_memory=False)
usn_df["UpdateTimestamp"] = pd.to_datetime(usn_df["UpdateTimestamp"], errors="coerce")
corroborated = []
for entry in stomped_files:
filename = entry["file_path"].split("\\")[-1]
# Find USN entries for this file with BASIC_INFO_CHANGE
usn_matches = usn_df[
(usn_df["Name"] == filename) &
(usn_df["UpdateReasons"].str.contains("BASIC_INFO_CHANGE", na=False))
]
ifnot usn_matches.empty:
entry["usn_corroboration"] = True
entry["usn_change_times"] = usn_matches["UpdateTimestamp"].tolist()
entry["highest_confidence"] = "critical"
corroborated.append(entry)
print(f"[CORROBORATED] {filename} - USN Journal confirms "f"BASIC_INFO_CHANGE at {usn_matches['UpdateTimestamp'].iloc[0]}")
return corroborated
# Parse USN Journal (use MFTECmd or ANJP)# MFTECmd.exe -f "$J" --csv D:\Evidence\Parsed\ --csvf usn_parsed.csv
Step 5: Check ShimCache and Amcache for Timeline Validation
defcheck_shimcache_timeline(stomped_files, shimcache_csv):
"""Validate timestamps against ShimCache (AppCompatCache) entries.
ShimCache records the last modification time of executables
independently of NTFS timestamps, providing another corroboration point.
"""
shim_df = pd.read_csv(shimcache_csv, low_memory=False)
shim_df["LastModifiedTimeUTC"] = pd.to_datetime(
shim_df["LastModifiedTimeUTC"], errors="coerce"
)
for entry in stomped_files:
filepath = entry["file_path"]
shim_match = shim_df[
shim_df["Path"].str.lower() == filepath.lower()
]
ifnot shim_match.empty:
shim_time = shim_match["LastModifiedTimeUTC"].iloc[0]
si_modified = pd.to_datetime(entry.get("si_created"))
if pd.notna(shim_time) and pd.notna(si_modified):
delta = abs((shim_time - si_modified).days)
if delta > 30:
entry["shimcache_mismatch"] = True
entry["shimcache_time"] = str(shim_time)
print(f"[SHIMCACHE MISMATCH] {filepath}")
print(f" SI timestamp: {si_modified}")
print(f" ShimCache timestamp: {shim_time}")
print(f" Delta: {delta} days")
return stomped_files
Step 6: Generate a Timestomping Detection Report
import json
defgenerate_report(stomped_files, output_path):
"""Generate a structured JSON report of all timestomping detections."""
report = {
"report_title": "Timestomping Detection Analysis",
"generated_at": datetime.utcnow().isoformat() + "Z",
"mitre_technique": "T1070.006 - Indicator Removal: Timestomp",
"total_suspicious_files": len(stomped_files),
"critical_findings": len([f for f in stomped_files if f["highest_confidence"] == "critical"]),
"high_findings": len([f for f in stomped_files if f["highest_confidence"] == "high"]),
"medium_findings": len([f for f in stomped_files if f["highest_confidence"] == "medium"]),
"findings": stomped_files,
}
withopen(output_path, "w") as f:
json.dump(report, f, indent=2, default=str)
print(f"Report written to {output_path}")
print(f" Critical: {report['critical_findings']}")
print(f" High: {report['high_findings']}")
print(f" Medium: {report['medium_findings']}")
generate_report(stomped_files, "D:\\Evidence\\timestomping_report.json")
Verification
Confirm MFTECmd parses the $MFT without errors and produces both 0x10 (SI) and 0x30 (FN) timestamp columns
Create a test file and use a timestomping tool (e.g., NTimeStomp) in a lab to verify the detection logic catches the manipulation
Validate that the nanosecond-zeroed check does not produce excessive false positives on files created by installers that legitimately set timestamps
Cross-reference flagged files with the USN Journal to confirm BASIC_INFO_CHANGE events exist at the expected times
Verify ShimCache and Amcache timestamps provide independent corroboration of timeline inconsistencies
Test against known-clean system images to establish a false-positive baseline (some backup/imaging software legitimately resets timestamps)
Confirm the detection pipeline correctly handles deleted MFT entries (InUse=false) which may contain evidence of timestomped files that were later removed
