Analyzing Mft For Deleted File Recovery

Analyzing MFT for Deleted File Recovery

Overview

The NTFS Master File Table ($MFT) is the central metadata repository for every file and directory on an NTFS volume. Each file is represented by at least one 1024-byte MFT record containing attributes such as $STANDARD_INFORMATION (timestamps, permissions), $FILE_NAME (name, parent directory, timestamps), and $DATA (file content or cluster run pointers). When a file is deleted, its MFT record is marked as inactive (InUse flag cleared) but the metadata remains until the entry is reallocated by a new file. This persistence makes MFT analysis a primary technique for recovering deleted file evidence, reconstructing file system timelines, and detecting anti-forensic activity such as timestomping.

Prerequisites

• Forensic disk image (E01, raw/dd, VMDK, or VHDX format)
• MFTECmd (Eric Zimmerman) or analyzeMFT (Python-based)
• FTK Imager, Arsenal Image Mounter, or similar for image mounting
• Timeline Explorer or Excel for CSV analysis
• Python 3.8+ for custom analysis scripts
• Understanding of NTFS file system internals

MFT Structure and Record Layout

MFT Record Header

Each MFT record begins with the signature "FILE" (0x46494C45) and contains:

Key MFT Attributes

Deleted File Recovery Techniques

Technique 1: MFT Record Analysis with MFTECmd

# Extract $MFT from forensic image using KAPE or FTK Imager
# Parse the $MFT with MFTECmd
MFTECmd.exe -f "C:\Evidence\$MFT" --csv C:\Output --csvf mft_full.csv

# Filter for deleted files (InUse = FALSE) in Timeline Explorer
# Look for entries where InUse column is False

Identifying Deleted Files in CSV Output:

• InUse = False indicates a deleted or reallocated record
• ParentPath shows original file location before deletion
• FileSize shows the original size (may still be recoverable)
• Timestamps in $STANDARD_INFORMATION and $FILE_NAME attributes persist

Technique 2: USN Journal ($UsnJrnl:$J) Analysis

The USN Journal records all changes to files on an NTFS volume, including creation, deletion, rename, and data modification events.

# Parse USN Journal with MFTECmd
MFTECmd.exe -f "C:\Evidence\$J" --csv C:\Output --csvf usn_journal.csv

# Key USN reason codes for deletion evidence:
# USN_REASON_FILE_DELETE     = 0x00000200
# USN_REASON_CLOSE           = 0x80000000
# USN_REASON_RENAME_OLD_NAME = 0x00001000
# USN_REASON_RENAME_NEW_NAME = 0x00002000

Technique 3: $LogFile Transaction Analysis

The $LogFile stores NTFS transaction records that can reveal file operations even after the USN Journal has been cycled.

# Parse $LogFile with LogFileParser
LogFileParser.exe -l "C:\Evidence\$LogFile" -o C:\Output

# Look for REDO and UNDO operations indicating file deletion:
# - DeallocateFileRecordSegment
# - DeleteAttribute
# - UpdateResidentValue (clearing InUse flag)

Technique 4: MFT Slack Space Analysis

MFT slack space exists between the end of the used portion of an MFT record and the end of the allocated 1024 bytes. This area may contain remnants of previous file records.

import struct

def parse_mft_slack(mft_path: str, output_path: str):
    """Extract and analyze MFT slack space for deleted file remnants."""
    with open(mft_path, "rb") as f:
        record_size = 1024
        record_num = 0
        slack_findings = []

        while True:
            record = f.read(record_size)
            if len(record) < record_size:
                break

            # Verify FILE signature
            if record[:4] != b"FILE":
                record_num += 1
                continue

            # Get used size from offset 0x18
            used_size = struct.unpack("<I", record[0x18:0x1C])[0]

            if used_size < record_size:
                slack = record[used_size:]
                # Check if slack contains readable strings or attribute headers
                if any(c > 0x20 and c < 0x7F for c in slack[:50]):
                    slack_findings.append({
                        "record": record_num,
                        "used_size": used_size,
                        "slack_size": record_size - used_size,
                        "slack_preview": slack[:100].hex()
                    })

            record_num += 1

    return slack_findings

Correlation with Supporting Artifacts

Cross-Reference MFT with $Recycle.Bin

# Parse Recycle Bin with RBCmd
RBCmd.exe -d "C:\Evidence\$Recycle.Bin" --csv C:\Output --csvf recycle_bin.csv

# Correlate: $I files contain original path and deletion timestamp
# Match MFT entry numbers from $R files back to original MFT records

Cross-Reference MFT with Volume Shadow Copies

# List volume shadow copies
vssadmin list shadows

# Mount shadow copies and extract $MFT from each
# Compare MFT records across shadow copies to track file changes over time

Forensic Value

• Deleted file metadata recovery: Original filename, path, size, and timestamps
• Timeline reconstruction: File creation, modification, access, and deletion events
• Timestomping detection: Comparing $SI vs $FN timestamps
• Data carving guidance: MFT cluster runs point to file content on disk
• Anti-forensic detection: Identifying wiped or manipulated MFT records

References

• NTFS MFT Advanced Forensic Analysis: https://www.deaddisk.com/posts/ntfs-mft-advanced-forensic-analysis-guide/
• MFT Slack Space Forensic Value: https://www.sygnia.co/blog/the-forensic-value-of-mft-slack-space/
• MFTECmd Documentation: https://ericzimmerman.github.io/
• SANS FOR500: Windows Forensic Analysis