File Entropy Calculation

TL;DR

This guide shows you how to calculate file entropy using common command-line tools like md5sum, sha256sum and a Python script. Entropy is a measure of randomness in data – useful for identifying potentially encrypted files or detecting changes.

Calculating File Entropy

1. Understanding Entropy: Entropy measures the unpredictability of information. Higher entropy means more random data, lower entropy means more predictable data.
2. Using Checksums (Quick Estimate): While not a direct entropy calculation, checksums like MD5 and SHA256 can give you a very rough idea if a file is significantly different from what it should be. If the checksum changes drastically, it suggests altered content.
   • MD5:

     md5sum filename

   • SHA256:

     sha256sum filename

   Note: MD5 is considered cryptographically broken and should not be used for security purposes. SHA256 is more secure but still doesn’t give you entropy.

3. Using Python (Precise Calculation): A Python script provides a reliable way to calculate file entropy.
   1. Install Python: Ensure you have Python 3 installed on your system.
   2. Create the Script: Save the following code as a Python file (e.g., entropy_calculator.py):

      import math
      
      def calculate_entropy(filename):
          with open(filename, 'rb') as f:
              data = f.read()
      
          if not data:
              return 0  # Handle empty files
      
          byte_counts = {}
          for byte in data:
              byte_counts[byte] = byte_counts.get(byte, 0) + 1
      
          total_bytes = len(data)
          entropy = 0.0
          for count in byte_counts.values():
              probability = float(count) / total_bytes
              entropy -= probability * math.log2(probability)
      
          return entropy
      
      if __name__ == "__main__":
          filename = input("Enter the filename: ")
          try:
              entropy = calculate_entropy(filename)
              print(f"Entropy of {filename}: {entropy} bits per byte")
          except FileNotFoundError:
              print(f"File not found: {filename}")

   3. Run the Script: Open a terminal and run the script using:

      python entropy_calculator.py

      The script will prompt you for the filename.

4. Interpreting Entropy Values:
   • Low Entropy (0-3 bits/byte): Highly predictable data, likely text or compressed files.
   • Medium Entropy (3-5 bits/byte): Some randomness, could be images, audio, or moderately complex data.
   • High Entropy (7+ bits/byte): Very random data, often indicates encrypted files or strong compression. Files close to 8 bits/byte are considered very random.