--- name: hunting-for-defense-evasion-via-timestomping-2f0a53ba494e 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. ' domain: cybersecurity subdomain: threat-hunting tags: - timestomping - ntfs-forensics - mft-analysis - defense-evasion version: '1.0' author: Krishna Kumar (xalgord) license: Apache-2.0 d3fend_techniques: - File Metadata Consistency Validation - Content Format Conversion - File Content Analysis - Platform Hardening - File Format Verification nist_csf: - DE.CM-01 - DE.AE-02 - DE.AE-07 - ID.RA-05 --- # 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, $LogFile, and ShimCache/Amcache analysis for corroboration. ## Detection Gaps & Validation - **The SI-vs-FN comparison fails against full forgery.** SetMACE and raw-disk tools (or `$FILE_NAME` manipulation via directory rename tricks) can align both `$STANDARD_INFORMATION` (0x10) and `$FILE_NAME` (0x30), so SI /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: ```powershell # Parse MFT to CSV with all timestamp columns MFTECmd.exe -f "D:\Evidence\$MFT" --csv D:\Evidence\Parsed\ --csvf mft_parsed.csv # The output CSV contains these critical columns: # Created0x10 - $STANDARD_INFORMATION Created timestamp # LastModified0x10 - $STANDARD_INFORMATION Modified timestamp # LastAccess0x10 - $STANDARD_INFORMATION Accessed timestamp # LastRecordChange0x10 - $STANDARD_INFORMATION Entry Modified timestamp # Created0x30 - $FILE_NAME Created timestamp # LastModified0x30 - $FILE_NAME Modified timestamp # LastAccess0x30 - $FILE_NAME Accessed timestamp # LastRecordChange0x30 - $FILE_NAME Entry Modified timestamp ``` ### 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: ```python import pandas as pd from datetime import datetime, timedelta def load_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 def detect_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 Created if 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 created if 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 nanoseconds if 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" not in 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 years if abs((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 rewritten if 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 in sorted(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: ```python def correlate_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)) ] if not 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 ```python def check_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() ] if not 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 ```python import json def generate_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, } with open(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 --- Source attribution: xalgord Original project: https://github.com/xalgord/xalgorix/blob/cec44d71b1d998208e1e2091982d0ee2f928041f/internal/tools/skills/data/threat-hunting/hunting-for-defense-evasion-via-timestomping/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: xalgord Original project: https://github.com/xalgord/xalgorix/blob/cec44d71b1d998208e1e2091982d0ee2f928041f/internal/tools/skills/data/threat-hunting/hunting-for-defense-evasion-via-timestomping/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Security Engineer Specialties: Backend Tools: Analyzemft, Ftkimager, Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: e17612e249bf863a4cc931f93117b20294759c53970d156a4cacad3a59292b56