skip to navigation
skip to content

Not Logged In

entDevType 0.1.1

A module for calculating the entropy/entropic deviations in data

EntropyDeviationType is an extension that is intended for finding data hidden within other data with no knowledge of the data itself. Specifically, the intended use case is to identify executable files (Portable Executables specifically) embedded in non-executable files. For example, malware hidden within a Microsoft Word or PDF document. This is a common occurrence within Advanced Persistent Threat (APT) style attacks which leverage client-side attacks in common business office file formats and often follow the generic pattern that within the exploit is a XOR encrypted executable that is dropped to the compromised system and then the host document is cleaned to remove the exploit.

The module contains two classes, entropyDeviationType and xorTableSearchType. Both classes are intended as proof of concepts and not immediately exportable to production. This package also contains an example utility, edfind.py, which serves as both an immediately usable utility and as a rough primer on how to use the extension to quickly analyze and locate rogue data hidden within benign information streams.

DISCLAIMER

YOUR MILEAGE MAY VARY. AS WITH EVERYTHING TEST THOROUGHLY YOURSELF BEFORE UTILIZING IN PRODUCTION CODE. THIS MODULE HAS NOT RECEIVED EXTENSIVE TESTING AND MAY CONTAIN BUGS NO WARRANTY, EXPLICIT OR IMPLICIT IS PROVIDED. ITS THE INTERNET. TRUST BUT VERIFY

BUILDING

  • Requires:
    • C++ compiler that supports C++11
    • Python >2.3 & <3.0 (tested only on 2.7)
    • The boost::python library

$ ./setup.py build # ./setup.py install

The C++ classes can be extracted and utilized with only a C++ compiler that supports C++11.

MORE INFORMATION

Included with this distribution is a PDF file in the ./doc/ directory that contains fairly verbose documentation that outlined both the Python and C++ API, structure and intended usage. It further outlines usage of the included example utility, edfind.py, and does so by explaining its usage on example document files.

In short, I really tried to type this all up in reST format, but that is just nuts. I instead elected to have a text file that provides a very basic description, that will play friendly with 80x60 terminals and a PDF document that describes everything in detail that doesn’t have to overly worry a whole lot about your particular environment for viewing the data. Cheers.

 
File Type Py Version Uploaded on Size
entDevType-0.1.1.tar.gz (md5) Source 2014-08-11 912KB
  • Downloads (All Versions):
  • 2 downloads in the last day
  • 20 downloads in the last week
  • 62 downloads in the last month