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Analysis tool for estimating the likelihood that a binary contains compressed or encrypted bytes

Project description

PyPi Build Status Python Versions Requirements Status Known Vulnerabilities License

Introduction

This tool is an implementation in Python of Bintropy, an analysis tool presented in this paper in the scope of packing detection based on entropy. It implements both modes of operation and an additional one, respectively on the entire binary, per section or per segment. It uses the entropy values mentioned in the paper for deciding whether the binary contains compressed/encrypted bytes.

It relies on lief for abstracting either PE, ELF or Mach-O executables. This tool thus supports these three formats.

Setup

This tool is available as a package from PyPi.

$ pip install bintropy

Usage

The help message explains every option.

$ bintropy --help

Modes of operation

Use the -m/--mode option.

  • 0: full binary (default)
  • 1: per section
  • 2: per segment

Note that mode 2 will logically give results very similar to mode 0.

$ bintropy binary
<<< boolean >>>

$ bintropy binary --dot-not-decide
<<< highest block entropy, average block entropy >>>
$ bintropy binary --mode [1|2]
<<< boolean >>>

$ bintropy binary -m [1|2] --do-not-decide
<<< highest block entropy, average block entropy >>>

Benchmarking

Use the -b/--benchmark option to get one more value, the processing time in seconds.

$ bintropy binary -b
<<< boolean, processing time >>>

$ bintropy binary -b --do-not-decide
<<< highest block entropy, average block entropy, processing time >>>

Overriding default entropy values

The reference paper uses 6.677 for the average block entropy and 7.199 for the highest block entropy (obtained by analyzing a dataset of PE files and using the first mode of operation). These values can be overriden with the dedicated options.

$ bintropy binary --threshold-average-entropy 5.678 --threshold-highest-entropy 6.789
[...]

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